a semiparametric approach to short term oil price forecasting abstract in this paper it is shown how the garch properties of oil price changes can be employed to forecast the oil price distribution over short term horizons the forecasting methodology is semiparametric and it is based on the bootstrap approach the results of an out of sample forecasting exercise carried out using the brent oil price series suggest that the forecasting approach can be used to obtain a performance measure for the forward price in addition to compute interval forecasts for the oil price

forecasting energy markets using support vector machines abstract in this paper we investigate the efficiency of a support vector machine svm based forecasting model for the next day directional change of electricity prices we first adjust the best autoregressive svm model and then we enhance it with various related variables the system is tested on the daily phelix index of the german and austrian control area of the european energy exchange ee wholesale electricity market the forecast accuracy we achieved is 76 12 over a 200 day period

output prices and the velocity of money in search equilibrium a monetary search model with match specific preferences and traded quantities is analyzed to assess how aggregate output the price level and the velocity of money are affected by various forms of technological change changes in the productive technology lead to output and prices moving in opposite directions but provide no prediction for monetary velocity the matching technology directly affects monetary velocity and output but has little impact on prices transactions technologies which affect the fixed cost of trading are ambiguous with respect to comovements of output and prices but systematically impact velocity

genco s risk constrained hydrothermal scheduling this paper presents a stochastic midterm risk constrained hydrothermal scheduling algorithm in a generation company genco the objective of a genco is to maximize payoffs and minimize financial risks when scheduling its midterm generation of thermal cascaded hydro and pumped storage units the proposed schedule will be used by the genco for bidding purposes to the iso the optimization model is based on stochastic price based unit commitment the proposed genco solution may be used to schedule midterm fuel and natural water inflow resources for a few months to a year the proposed stochastic mixed integer programming solution considers random market prices for energy and ancillary services as well as the availability of natural water inflows and generators in monte carlo scenarios financial risks associated with uncertainties are considered by applying expected downside risks which are incorporated explicitly as constraints variable time steps are adopted to avoid the exponential growth in solution time and memory requirements when considering midterm constraints a single water to power conversion function is used instead of several curves for representing water head and discharge parameters piecewise linearized head dependant water to power conversion functions are used for computational efficiency illustrative examples examine gencos midterm generation schedules risk levels fuel and water usage and hourly generation dispatches for bidding in energy and ancillary services markets the paper shows that gencos could decrease their financial risks by adjusting expected payoffs

mixed models for short run forecasting of electricity prices application for the spanish market short run forecasting of electricity prices has become necessary for power generation unit schedule since it is the basis of every profit maximization strategy in this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed the main idea is to develop an efficient tool for one step ahead forecasting in the future combining several prediction methods for which forecasting performance has been checked and compared for a span of several years also as a novelty the 24 hourly time series has been modelled separately instead of the complete time series of the prices this allows one to take advantage of the homogeneity of these 24 time series the purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting these results have been obtained by means of a computational experiment a mixed model which combines the advantages of the two new models discussed is proposed some numerical results for the spanish market are shown but this new methodology can be applied to other electricity markets as well

forecasting electricity prices and their volatilities using unobserved components abstract the liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities both in the short and long term thus there is a need of providing methodology that is able to deal with the most important features of electricity price series which are well known for presenting not only structure in conditional mean but also time varying conditional variances in this work we propose a new model which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices these common factors are jointly estimated as well as their relationship with the original vector of series and the dynamics affecting both their conditional mean and variance the estimation of the model is carried out under the state space formulation the new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the spanish zone of the iberian market several simplified alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power

robust estimation and forecasting of the long term seasonal component of electricity spot prices when building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data while the short term seasonal components daily weekly are more regular and less important for valuation of typical power derivatives the l ong term seasonal components ltsc seasonal annual are much more diffi cult to tackle surprisingly in many academic papers dealing with electricity spot price modeling the importance of the seasonal decomposition is neglected and the problem of forecasting it is not considered with this paper we want to fill the gap and present a thorough study on estimation and forecasting of the ltsc of electricity spot prices we consider a battery of models based on fourier or wavelet decomposition combined with linear or exponential decay we find that all considered wavelet base d models are significantly better in terms of forecasting spot prices up to a year ahead than all considered sine based models this result questions the validity and usefulness of stochastic models of spot electricity prices built on sinusoidal long term seasonal components

a multimarket decision making framework for genco considering emission trading scheme in a multimarket environment a genco produces electricity subject to a number of factors including physical and environmental constraints together with trading strategies in the electricity market em fuel market fm and carbon market cm to assist a genco to maximize its profits from em fm and cm this paper proposes a dynamic decision making model with two consecutive stages fuzzy differential evolution algorithm is used to solve this decision making problem taking transactions in the three interactive markets into account the proposed model has been tested for a genco consisting of seven thermal units and a wind farm a rational tradeoff between the profit making and emission reduction has been demonstrated by the genco using the proposed model indicating a well alignment with the intended goal of the introducing emission trading scheme ets

an adaptive wavelet neural network based energy price forecasting in electricity markets in a competitive electricity market an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions an adaptive wavelet neural network awnn is proposed in this paper for short term price forecasting stpf in the electricity markets a commonly used mexican hat wavelet has been chosen as the activation function for hidden layer neurons of feed forward neural network ffnn to demonstrate the effectiveness of the proposed approach day ahead prediction of market clearing price mcp of spain market which is a duopoly market with a dominant player and locational marginal price lmp forecasting in pjm electricity market are considered the forecasted results clearly show that awnn has good prediction properties compared to other forecasting techniques such as wavelet arima multi layer perceptron mlp and radial basis function rbf neural networks as well as recently proposed fuzzy neural network fnn

forward contracting and selling price determination for a retailer to procure the electric energy to be sold to its clients a retailer has to face two major difficulties while buying electric energy it must cope with uncertain pool prices or sign forward contracts at higher average prices while selling electricity it should handle the uncertainty of the end user demand and the fact that customers might choose a different retailer if the selling price is not competitive enough we propose a risk constrained stochastic programming framework to decide which forward contracts the retailer should sign and at which price it must sell electricity so that its expected profit is maximized at a given risk level the developed model takes into account the elastic behavior of end users with respect to the selling price offered by the retailer a realistic case study illustrates the methodology proposed

a search theoretic approach to monetary economics the essential function of money is its role as a medium of exchange the authors formalize this idea using a search theoretic equilibrium model of the exchange process that captures the double coincidence of wants problem with pure barter one advantage of the framework described here is that it is very tractable the authors also show that the model can be used to addre ss some substantive issues in monetary economics including the potenti al welfare enhancing role of money the interaction between specializat ion and monetary exchange and the possibility of equilibria with multip le fiat currencies copyright 1993 by american economic association

electricity price forecasting using sale and purchase curves the x model abstract our paper aims to model and forecast the electricity price by taking a completely new perspective on the data it will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis instead of directly modeling the electricity price as it is usually done in time series or data mining approaches we model and utilize its true source the sale and purchase curves of the electricity exchange we will refer to this new model as x model as almost every deregulated electricity price is simply the result of the intersection of the electricity supply and demand curve at a certain auction therefore we show an approach to deal with a tremendous amount of auction data using a subtle data processing technique as well as dimension reduction and lasso based estimation methods we incorporate not only several known features such as seasonal behavior or the impact of other processes like renewable energy but also completely new elaborated stylized facts of the bidding structure our model is able to capture the non linear behavior of the electricity price which is especially useful for predicting huge price spikes using simulation methods we show how to derive prediction intervals for probabilistic forecasting we describe and show the proposed methods for the day ahead epex spot price of germany and austria

the rise fall and rise again of industrial location theory m ccann p and sheppard s 2003 the rise fall and rise again of industrial location theory reg studies 37 649 663 in this paper we will argue that new academic fashions new international institutional arrangements new communications technology and new developments in data availability have all renewed the need for a redevelopment of analytical industrial location theory our paper will argue that the microeconomic foundations of industrial location theory must now be reconsidered in particular the methodological basis of traditional industrial location models needs to be reconciled with recent models of clustering the new economic geography literature and also more aggregate systemic levels of analysis we will argue that in order to do this it is necessary first to specify the transactions cost assumptions underlying these various approaches second to adopt broader definitions of spatial transactions costs and third to incorporate environmental characteristics within an orthodox loca

selecting input factors for clusters of gaussian radial basis function networks to improve market clearing price prediction in a deregulated power market bidding decisions rely on good market clearing price prediction one of the common forecasting methods is gaussian radial basis function grbf networks that approximate input output relationships by building localized gaussian functions clusters currently a cluster uses all the input factors certain input factors however may not be significant and should be deleted because they mislead local learning and result in poor predictions existing pruning methods for neural networks examine the significance of connections between neurons and are not applicable to deleting center and standard deviation parameters in a grbf network since those parameters bear no sense of significance of connection in this paper the inverses of standard deviations are found to capture a sense of connection and based on this finding a new training method to identify and eliminate unimportant input factors is developed numerical testing results from two classroom problems and from new england market clearing price prediction show that the new training method leads to significantly improved prediction performance with a smaller number of network parameters

barter and monetary exchange under private information the authors develop a model of production and exchange with uncertainty concerning the quality of commodities and study the role of fiat money in ameliorating frictions caused by private information the model is specified so that without private information only high quality commodities are produced and there is no welfare gain from using money with private information there can be equilibria and sometimes multiple equilibria where low quality commodities are produced and money can increase welfare money works by promoting useful production and exchange in efficient monetary equilibria agents adopt strategies that increase the probability of acquiring high quality output copyright 1994 by american economic association

a divisible search model of fiat money this paper extends the kiyotaki wright search model of fiat money to allow for divisible money and goods the extension allows me to examine the standard issues in monetary economics such as the neutrality and superneutrality of money by severing the artificial link in the kiyotaki wright model between the money supply and the number of money holders it is shown that money is neutral but not superneutral money growth generates a trading opportunity effect it changes the fraction of different agents in the economy and hence changes the probability with which agents have a successful match in addition money growth has a negative effect on the real money balance that is familiar in walrasian monetary models the balance of the two effects can imply a positive optimal money growth rate

money and the gains from trade this article studies the role of money in environments where in each meeting there is a double coincidence of real wants traders who meet at random finance their purchases through current production the sale of divisible money or both it is shown that in the absence of valued money if traders have asymmetric tastes for each other s good they produce and exchange socially inefficient quantities with valued money however traders exchange efficient quantities if the asymmetry of tastes is not too large it is shown that the gains from trade in the monetary economy are strictly greater than those in the corresponding barter economy that the friedman rule holds and that the allocation of resources in the monetary economy converges to the allocation in the barter economy as the growth rate of the money supply is increased

day ahead price forecasting of electricity markets by mutual information technique and cascaded neuro evolutionary algorithm in a competitive electricity market price forecasts are important for market participants however electricity price is a complex signal due to its nonlinearity nonstationarity and time variant behavior in spite of much research in this area more accurate and robust price forecast methods are still required in this paper a combination of a feature selection technique and cascaded neuro evolutionary algorithm cnea is proposed for this purpose the feature selection method is an improved version of the mutual information mi technique the cnea is composed of cascaded forecasters where each forecaster consists of a neural network nn and an evolutionary algorithm ea an iterative search procedure is also incorporated in our solution strategy to fine tune the adjustable parameters of both the mi technique and cnea the price forecast accuracy of the proposed method is evaluated by means of real data from the pennsylvania new jersey maryland pjm and spanish electricity markets the method is also compared with some of the most recent price forecast techniques

compensation for quality difference in a search model of money we study an economy in which there is always double coincidence of wants agents have perfect information about qualities of goods and there are no transaction costs the hold up problem arises because efforts invested in improving quality prior to search may not be compensated in the market situations in which barter fails to motivate quality improvement are identified with money however the extra effort in quality improvement will be compensated when high quality good producers trade with agents holding both the low quality good and money injection of money can induce almost all agents to produce the high quality good

a framework for electricity price spike analysis with advanced data mining methods there are many techniques for electricity market price forecasting however most of them are designed for expected price analysis rather than price spike forecasting an effective method of predicting the occurrence of spikes has not yet been observed in the literature so far in this paper a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes combined with the spike value prediction techniques developed by the same authors the proposed approach aims at providing a comprehensive tool for price spike forecasting in this paper feature selection techniques are first described to identify the attributes relevant to the occurrence of spikes a simple introduction to the classification techniques is given for completeness two algorithms support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in detail realistic market data are used to test the proposed model with promising results

time dependent correlations in electricity markets in the last years many electricity markets were subjected to deregulated operation where prices are set by the action of market participants in this form producers and consumers rely on demand and price forecasts to decide their bidding strategies allocate assets negotiate bilateral contracts hedge risks and plan facility investments a basic feature of efficient market hypothesis is the absence of correlations between price increments over any time scale leading to random walk type behavior of prices so arbitrage is not possible however recent studies have suggested that this is not the case and correlations are present in the behavior of diverse electricity markets in this paper a temporal quantification of electricity market correlations is made by means of detrended fluctuation and allan analyses the approach is applied to two canadian electricity markets ontario and alberta the results show the existence of correlations in both demand and prices exhibiting complex time dependent behavior with lower correlations in winter while higher in summer relatively steady annual cycles in demand but unstable cycles in prices are detected on the other hand the more significant nonlinear effects measured in terms of a multifractality index are found for winter months while the converse behavior is displayed during the summer period in terms of forecasting models our results suggest that nonlinear recursive models e g feedback nns should be used for accurate day ahead price estimation in contrast linear models can suffice for demand forecasting purposes

a hybrid model for day ahead price forecasting this paper presents a hybrid time series and adaptive wavelet neural network awnn model for the day ahead electricity market clearing price forecast instead of using price series one period continuously compounded return series is used to achieve more attractive statistical properties the autoregressive moving average with exogenous variables armax model is used to catch the linear relationship between price return series and explanatory variable load series the generalized autoregressive conditional heteroscedastic garch model is used to unveil the heteroscedastic character of residuals and awnn is used to present the nonlinear nonstationary impact of load series on electricity prices the monte carlo method is adopted to generate more evenly distributed random numbers used for time series and awnn models to accelerate the convergence several criteria such as average mean absolute percentage error amape and the variance of forecast errors are used to assess the model and measure the forecasting accuracy illustrative price forecasting examples of the pjm market are presented to show the efficiency of the proposed method

dynamic monetary equilibrium in a random matching economy this article concerns an infinite horizon economy where trade must occur pairwise using a double auction mechanism and where fiat money overcomes lack of double coincidence of wants traders are anonymous and lack market power goods are divisible and perishable and are consumed at every date preferences are defined by utility stream overtaking money is divisible and not subject to inventory constraints the evolution of individual and economywide money holdings distributions is characterized there is a welfare ordered continuum of single price equilibria reflecting indeterminacy of the price level rather than of relative prices

efficient modeling and forecasting of electricity spot prices abstract the increasing importance of renewable energy especially solar and wind power has led to new forces in the formation of electricity prices hence this paper introduces an econometric model for the hourly time series of electricity prices of the european power exchange epex which incorporates specific features like renewable energy the model consists of several sophisticated and established approaches and can be regarded as a periodic var tarch with wind power solar power and load as influences on the time series it is able to map the distinct and well known features of electricity prices in germany an efficient iteratively reweighted lasso approach is used for the estimation moreover it is shown that several existing models are outperformed by the procedure developed in this paper

energy models for demand forecasting a review abstract energy is vital for sustainable development of any nation be it social economic or environment in the past decade energy consumption has increased exponentially globally energy management is crucial for the future economic prosperity and environmental security energy is linked to industrial production agricultural output health access to water population education quality of life etc energy demand management is required for proper allocation of the available resources during the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs in this paper an attempt is made to review the various energy demand forecasting models traditional methods such as time series regression econometric arima as well as soft computing techniques such as fuzzy logic genetic algorithm and neural networks are being extensively used for demand side management support vector regression ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting bottom up models such as markal and leap are also being used at the national and regional level for energy demand management

which way the natural gas price an attempt to predict the direction of natural gas spot price movements using trader positions abstract this research provides a method of predicting direction of spot price movements in the natural gas market for the month succeeding from market participants positions in the futures market cumby and modest cumby r e modest d m 1987 testing for market timing ability a framework for forecast evaluation journal of financial economics 19 169 189 provide the backdrop for analyzing the futures market positions of large hedgers and speculators to arrive at conclusions of market price movements in the spot market this methodology is suggested as a means for municipalities entering the natural gas market to improve upon their ordering of quantities of gas for the ensuing months in order to take advantage of possibly foreseeable price trends

application of public domain market information to forecast ontario s wholesale electricity prices this paper evaluates the usefulness of publicly available electricity market information in forecasting the hourly ontario energy price hoep in order to do so relevant data from ontario and its neighboring electricity markets namely new york new england and pjm electricity markets are investigated and a final set of explanatory variable candidates that are available before real time are selected multivariate transfer function and dynamic regression models are employed to relate hoep behavior to the selected explanatory variable candidates univariate arima models are also developed for the hoep the hoep models are developed on the basis of two forecasting horizons i e 3 h and 24 h and forecasting performance of the multivariate models is compared with that of the univariate models the outcomes show that the market information publicly available before real time can be used to improve hoep forecast accuracy to some extent however unusually high or low prices remain unpredictable and hence the available data cannot lead to significantly more accurate forecasts nevertheless the generated forecasts in this paper are significantly more accurate than currently available hoep forecasts to analyze the relatively low accuracy of the hoep forecasts comparisons are made with respect to arima models developed for locational marginal prices lmps of ontario s three neighboring markets and price volatility analyses are presented

growth pole strategies in regional economic planning a retrospective view part 2 implementation and outcome the paper continues from part 1 which appeared in the previous issue of the journal the primary concern is with neglected aspects of the growth pole strategy particularly as these relate to its i

on the efficiency of monetary exchange how divisibility of money matters abstract we use alternative assumptions about the divisibility of goods and money and the ability of agents to use lotteries on money to investigate to what extent the indivisibility of money is the cause for the typically inefficient production and consumption decisions in search theoretic models of money our framework potentially generates three types of inefficiencies the no trade inefficiency where no trade takes place even though it would be socially efficient to trade and the too much trade and too little trade inefficiencies where the quantities produced and exchanged are either larger or smaller than what the solution to a social planner s problem would mandate it is shown that while the no trade and the too much trade inefficiencies are caused by the indivisibility of money the too little trade inefficiency remains even when money is divisible unless it is sufficiently valued

fiat money and quality uncertainty the paper studies the role of money as a medium of exchange when barter trade is restricted not only by the double coincidence of wants requirement but also by private information about the quality of commodities for this purpose the model of kiyotaki and wright 1989 is extended to include commodities of both high and low quality it is shown that despite being intrinsically useless fiat money may have value in trade because it is of uniform quality moreover the introduction of a sufficiently small amount of fiat money proves to be welfare increasing

how does germany s green energy policy affect electricity market volatility an application of conditional autoregressive range models abstract based on a dynamic model for the high low range of electricity prices this article analyses the effects of germany s green energy policy on the volatility of the electricity market using european energy exchange data from 2000 to 2015 we find rather high volatility in the years 2000 2009 but also that the weekly price range has significantly declined in the period following the year 2009 this period is characterised by active regulation under the energy industry law enwg the eu emissions trading directive etd and the renewable energy law eeg in contrast to the preceding period price jumps are smaller and less frequent especially for day time hours implying that current policy measures are effective in promoting renewable energies while simultaneously upholding electricity market stability this is because the regulations strive towards a more and more flexible and market oriented structure which allows better integration of renewable energies and supports an efficient alignment of renewable electricity supply with demand

modeling real time balancing power market prices using combined sarima and markov processes this paper describes modeling of real time balancing power market prices by using combined seasonal auto regressive integrated moving average sarima and discrete markov processes the combination of such processes allows generation of price series with periods where no demand for balancing power exists the purpose of the model is simulation of prices to construct scenario trees representing possible realization of the stochastic prices such scenario trees can be used in planning models based on stochastic optimization to generate bid sequences to the balancing market the spread of the prices in the tree and the shape of the scenarios are of central importance model parameter estimation methods reflecting the demands on scenario trees have therefore been used the proposed model is also applied to data from the nordic power market the conclusion of this paper is that the developed model is appropriate for modeling real time balancing power prices

on money as a medium of exchange we analyze economies in which individuals specialize in consumption and production and meet randomly over time in a way that implies that trade must be bilateral and quid pro quo nash equilibria in trading strategies are characterized certain goods emerge endogenously as media of exchange or commodity money depending both on their intrinsic properties and on extrinsic beliefs there are also equilibria with genuine fiat currency circulating as the general medium of exchange we find that equilibria are not generally pareto optimal and that introducing fiat currency into a commodity money economy may unambiguously improve welfare velocity acceptability and liquidity are discussed

forecasting electricity spot prices using lasso on capturing the autoregressive intraday structure in this paper we present a regression based model for day ahead electricity spot prices we estimate the considered linear regression model by the lasso estimation method the lasso approach allows for many possible parameters in the model but also shrinks and sparsifies the parameters automatically to avoid overfitting thus it is able to capture the autoregressive intraday dependency structure of the electricity price well we discuss in detail the estimation results which provide insights to the intraday behavior of electricity prices we perform an out of sample forecasting study for several european electricity markets the results illustrate well that the efficient lasso based estimation technique can exhibit advantages from two popular model approaches

bidding in sequential electricity markets the nordic case abstract for electricity market participants trading in sequential markets with differences in price levels and risk exposure it is relevant to analyze the potential of coordinated bidding we consider a nordic power producer who engages in the day ahead spot market and the hour ahead balancing market in both markets clearing prices and dispatched volumes are unknown at the time of bidding however in the balancing market the market participant faces an additional risk of not being dispatched taking into account the sequential clearing of these markets and the gradual realization of market prices we formulate the bidding problem as a multi stage stochastic program we investigate whether higher risk exposure may cause hesitation to bid into the balancing market furthermore we quantify the gain from coordinated bidding and by deriving bounds on this gain assess the performance of alternative bidding strategies used in practice

improving market clearing price prediction by using a committee machine of neural networks predicting market clearing prices is an important but difficult task and neural networks have been widely used a single neural network however may misrepresent part of the input output data mapping that could have been correctly represented by different networks the use of a committee machine composed of multiple networks can in principle alleviate such a difficulty a major challenge for using a committee machine is to properly combine predictions from multiple networks since the performance of individual networks is input dependent due to mapping misrepresentation this paper presents a new method in which weighting coefficients for combining network predictions are the probabilities that individual networks capture the true input output relationship at that prediction instant testing of the new england market cleaning prices demonstrates that the new method performs better than individual networks and better than committee machines using current ensemble averaging methods

a garch forecasting model to predict day ahead electricity prices price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets both for spot markets and long term contracts price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits this paper provides an approach to predict next day electricity prices based on the generalized autoregressive conditional heteroskedastic garch methodology that is already being used to analyze time series data in general a detailed explanation of garch models is presented and empirical results from the mainland spain and california deregulated electricity markets are discussed

hmm filtering and parameter estimation of an electricity spot price model abstract in this paper we develop a model for electricity spot price dynamics the spot price is assumed to follow an exponential ornstein uhlenbeck ou process with an added compound poisson process in this way the model allows for mean reversion and possible jumps all parameters are modulated by a hidden markov chain in discrete time they are able to switch between different economic regimes representing the interaction of various factors through the application of reference probability technique adaptive filters are derived which in turn provide optimal estimates for the state of the markov chain and related quantities of the observation process the em algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters we implement this self calibrating model on a deseasonalised series of daily spot electricity prices from the nordic exchange nord pool on the basis of one step ahead forecasts we found that the model is able to capture the empirical characteristics of nord pool spot prices

selling global buying local what determines the sourcing patterns of multinational plants in ireland hewitt dundas n andreosso o callaghan b crone m murray j and roper s 2005 selling global buying local what determines the sourcing patterns of multinational plants in ireland regional studies 39 225 239 multinational enterprises mnes have played an important if not crucial role in the recent development of the economy of ireland both north and south this paper compares the extent to which mne plants north and south are integrated into their host economies through local sourcing this is important both in terms of the direct effect on local demand and because local trading relationships provide a medium for technology transfer and supplier development levels of local sourcing north and south have diverged sharply over the last 15 years in the south local supply chains have grown stronger with mne plants now purchasing around 21 of their material inputs from within the country in the north by contrast local supply chain linkages have become weaker with only around 11 of mn

recent advances in electricity price forecasting a review of probabilistic forecasting abstract since the inception of competitive power markets two decades ago electricity price forecasting epf has gradually become a fundamental process for energy companies decision making mechanisms over the years the bulk of research has concerned point predictions however the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply demand and prices academics and practitioners alike have come to understand that probabilistic electricity price and load forecasting is now more important for energy systems planning and operations than ever before with this paper we offer a tutorial review of probabilistic epf and present much needed guidelines for the rigorous use of methods measures and tests in line with the paradigm of maximizing sharpness subject to reliability the paper can be treated as an update and a further extension of the otherwise comprehensive epf review of weron 1 or as a standalone treatment of a fascinating and underdeveloped topic that has a much broader reach than epf itself

arima models to predict next day electricity prices price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets both for spot markets and long term contracts price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit this paper provides a method to predict next day electricity prices based on the arima methodology arima techniques are used to analyze time series and in the past have been mainly used for load forecasting due to their accuracy and mathematical soundness a detailed explanation of the aforementioned arima models and results from mainland spain and californian markets are presented

reducing electricity price forecasting error using seasonality and higher order crossing information commodity prices are volatile and their volatility changes over time electricity prices are even more volatile in general because customers cannot easily switch to other energy source when the electricity prices fluctuate electricity prices also have strong seasonal behavior because the demand of electricity depends on the season to address these characteristics a time series forecasting model with seasonality and higher order crossing information for electricity prices is proposed the model measures the similarity between two time series by using their higher order crossing information the model can predict not only day ahead prices short term forecasting but also a series of prices for one week ahead or even longer simultaneously medium term price curve forecasting the model is tested on both high and low volatile data sets to evaluate its robustness and generality it is also shown that the model can produce more accurate prediction than some well known models

mutual productivity spillovers between foreign and local firms in china the existing literature treats advanced technology sourcing as the only cause of reverse productivity spillovers from local to foreign firms and implies that mutual spillovers between foreign and local firms can only happen in the developed world this paper argues that the diffusion of indigenous technology and local knowledge helps the productivity enhancement of multinationals so that there can be mutual spillovers even in a developing country the results from a large sample firm level econometric analysis and a comparative case study of seven companies in chinese manufacturing support this new argument as mutual spillovers are identified between local chinese firms and overseas chinese or oecd invested firms

residential appliances direct load control in real time using cooperative game the relatively fixed residential day ahead real time electricity price reflects insufficient information of the market so the response of residential energy management system ems to the real time pricing rtp is not complete and therefore retailers are exposed to the risk of price fluctuation in balance market direct load control dlc in cooperative game is proposed in this paper a cooperative game union comprised of some users and a retailer is established to minimize the union costs the union provides an opportunity to access the balance market indirectly for residents and to reduce risks and costs for the retailer in addition shapley value which embodies the fairness is used in union profits allocation the method that avoids bidding for residential users simplifies the thresholds of residents to participate in market furthermore the dlc union contributes to imbalance self management for the retailer which helps him avoid paying for the regulation cost involved in the deviation between the total quantity bought at markets and the actual consumption the achievement of union s goal respects the constraints set by users the method that alleviates the disturbance of dlc to residents meets the dual goals of being both fully responsive and non disruptive

electricity market price forecasting based on weighted nearest neighbors techniques this paper presents a simple technique to forecast next day electricity market prices based on the weighted nearest neighbors methodology first it is explained how the relevant parameters defining the adopted model are obtained such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction then results corresponding to the spanish electricity market during 2002 are presented and discussed finally the performance of the proposed method is compared with that of recently published techniques

hedging local volume risk using forward markets nordic case abstract with focus on the nordic electricity market this paper develops hedging strategies for an electricity distributor who manages price and volume risk from fixed price agreements on stochastic electricity load whereas the distributor trades in the spot market at area prices the financial contracts used for hedging are settled against the system price area and system prices are correlated with electricity load as are price differences in practice however this is often disregarded here we develop a joint model for the area price the system price and the load accounting for correlations and we suggest various strategies for hedging in the presence of local volume risk we benchmark against a strategy that ignores correlation and hedges at expected load as is common practice in the industry using data from 2013 and 2014 for two danish bidding areas we show that our best hedging strategy reduces gross loss by 5 8 and 13 6 and increases gross profit by 3 8 and 9 5 respectively although this is partly due to the inclusion of correlation we show that performance improvement is mainly driven by the choice of risk measure

economic impact of electricity market price forecasting errors a demand side analysis several techniques have been proposed in the literature to forecast electricity market prices and improve forecast accuracy however no studies have been reported examining the economic impact of price forecast inaccuracies on forecast users therefore in this paper the application of electricity market price forecasts to short term operation scheduling of two typical and inherently different industrial loads is examined and the associated economic impact is analyzed using electricity market price forecasts as the expected next day electricity prices optimal operating schedules and the associated costs are determined for each load these costs are compared with those of a perfect price forecast scenario in which actual prices are used to determine the operating schedules numerical results and discussions are provided based on price forecasts with different error characteristics

flexible transmission expansion planning with uncertainties in an electricity market deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult more flexible transmission expansion plans are needed to cope with the increased risks in this paper a novel planning approach is proposed to meet the above challenge in our approach the planning process is modeled as a mixed integer nonlinear programming minlp problem so that conflicting objectives can be optimized simultaneously to minimize planning risks our method identifies several scenarios based on statistics and expert knowledge the most flexible expansion plan is selected as the plan that has least adaptation cost the proposed method is tested with the ieee 14 bus system promising results are obtained to demonstrate the effectiveness of our method

classification of future electricity market prices forecasting short term electricity market prices has been the focus of several studies in recent years although various approaches have been examined achieving sufficiently low forecasting errors has not been always possible however certain applications such as demand side management do not require exact values for future prices but utilize specific price thresholds as the basis for making short term scheduling decisions in this paper classification of future electricity market prices with respect to pre specified price thresholds is introduced two alternative models based on support vector machines are proposed in a multi class multi step ahead price classification context numerical results are provided for classifying prices in ontario s and alberta s markets

just in time inventory control and the economic integration of japanese owned manufacturing plants with the county state and national economies of the united states reid n 1995 just in time inventory control and the economic integration of japanese owned manufacturing plants with the county state and national economies of the united states reg studies 29 345 355 this paper attempts to shed light on the ongoing debate concerning the spatial implications of just in time jit manufacturing systems the central issue around which this debate has focused concerns the extent to which use of jit inventory control encourages the spatial clustering of the members of a jit network this paper tests the hypothesis that plants which use jit inventory control purchase a greater proportion of their material inputs from inside their host economy than plants which do not use jit inventory control using survey data collected from 239 us based japanese owned manufacturing plants this hypothesis is tested for host economies defined at three geographic scales county state and national support for the hypothesis is found at the county but not at the state and national scales

a statistical approach for interval forecasting of the electricity price electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market rather than forecasting the value market participants are sometimes more interested in forecasting the prediction interval of the electricity price forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions in this paper a novel data mining based approach is proposed to achieve two major objectives 1 to accurately forecast the value of the electricity price series which is widely accepted as a nonlinear time series 2 to accurately estimate the prediction interval of the electricity price series in the proposed approach support vector machine svm is employed to forecast the value of the price to forecast the prediction interval we construct a statistical model by introducing a heteroscedastic variance equation for the svm maximum likelihood estimation mle is used to estimate model parameters results from the case studies on real world price data prove that the proposed method is highly effective compared with existing methods such as garch models

a novel approach to forecast electricity price for pjm using neural network and similar days method price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk and it also plays a key role in the economic optimization of the electric energy industry this paper explores a technique of artificial neural network ann model based on similar days sd method in order to forecast day ahead electricity price in the pjm market to demonstrate the superiority of the proposed model publicly available data acquired from the pjm interconnection were used for training and testing the ann the factors impacting the electricity price forecasting including time factors load factors and historical price factors are discussed comparison of forecasting performance of the proposed ann model with that of forecasts obtained from similar days method is presented daily and weekly mean absolute percentage error mape of reasonably small value and forecast mean square error fmse of less than 7 mwh were obtained for the pjm data which has correlation coefficient of determination of 0 6744 between load and electricity price simulation results show that the proposed ann model based on similar days method is capable of forecasting locational marginal price lmp in the pjm market efficiently and accurately

electricity price curve modeling and forecasting by manifold learning this paper proposes a novel nonparametric approach for the modeling and analysis of electricity price curves by applying the manifold learning methodology locally linear embedding lle the prediction method based on manifold learning and reconstruction is employed to make short term and medium term price forecasts our method not only performs accurately in forecasting one day ahead prices but also has a great advantage in predicting one week ahead and one month ahead prices over other methods the forecast accuracy is demonstrated by numerical results using historical price data taken from the eastern u s electric power markets

generation scheduling in a deregulated system the norwegian case the paper gives an overview of the deregulated electricity supply system in norway with emphasis on the generation scheduling approach procedures and tools for long medium and short term scheduling are described scheduling is carried out by each individual utility since scheduling is based on profit maximizing the spot market price is a very important factor this price is varying and uncertain however a part of the paper describes how to deal with this by considering the spot market price as an exogenously given stochastic variable it is shown how to extend the common water value computations so as to handle this case implications to the medium and short term scheduling are also discussed

short term congestion forecasting in wholesale power markets short term congestion forecasting is highly important for market participants in wholesale power markets that use locational marginal prices lmps to manage congestion accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning this study proposes a new short term forecasting algorithm for congestion lmps and other power system variables based on the concept of system patterns combinations of status flags for generating units and transmission lines the advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error the advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced forecasting results based on a nyiso case study demonstrate the feasibility and accuracy of the proposed algorithm

a contribution to the pure theory of money abstract this article analyzes a general equilibrium model with search frictions and differentiated commodities because of the many differentiated commodities barter is difficult in that it requires a double coincidence of wants and this provides a medium of exchange role for fiat currency we prove the existence of equilibrium with valued fiat money and show it is robust to certain changes in the environment rate of return dominance liquidity and the welfare implications of fiat money are discussed

forecasting next day electricity prices by time series models in the framework of competitive electricity markets power producers and consumers need accurate price forecasting tools price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities respectively this paper provides two highly accurate yet efficient price forecasting tools based on time series analysis dynamic regression and transfer function models these techniques are explained and checked against each other results and discussions from real world case studies based on the electricity markets of mainland spain and california are presented

energy price forecasting in the ontario competitive power system market this paper introduces a method for forecasting energy prices using artificial intelligence methods such as neural networks and fuzzy logic and a combination of the two the new approach is compared with some of the exiting methods various factors affecting the market clearing price are investigated results for the ontario electricity market are presented

commodity money under private information abstract private information is introduced into the kiyotaki wright model of commodity money in terms of qualitative uncertainty concerning the good which would be the unique medium of exchange under complete information producers are allowed to produce high or low quality versions of that good and people may not always recognize its quality it is shown that commodities that suffer from qualitative uncertainty may still be used as the medium of exchange when the private information problem is not too severe this is so despite the fact that in some equilibria there is necessarily some low quality commodity money in circulation when the information problem is so severe that the good of uncertain quality cannot be the unique money there can exist an equilibrium with dual commodity monies of which one is the good subject to qualitative uncertainty

search bargaining money and prices under private information this paper presents a model of money and search where bargaining determines prices and the quality of goods is private information it studies how a lemons problem affects the purchasing power of money there are multiple pareto ranked equilibria the superior equilibrium where no lemons are produced exists even if information about quality is relatively scarce in other equilibria there is price dispersion and uninformed buyers pay higher prices than informed buyers for all goods taxing money balances a proxy for inflation makes buyers less selective thus reducing the average quality of supply and the premium paid for known quality

money and information this paper investigates the role of money in markets in which producers have private information about the quality of the goods they supply when the fraction of high quality producers in the economy is given money promotes the production of high quality goods which improves the quality mix and welfare unambiguously when this fraction is endogenous however we find that money can decrease welfare relative to the barter equilibrium the origin of this inefficiency is that money provides consumption insurance to low quality producers which can result in a higher fraction of low quality producers in the monetary equilibrium finally we find that most often agents acquire more costly information in the monetary equilibrium than in the barter equilibrium consequently money is welfare enhancing because it promotes useful production and exchange but not because it saves information costs

impact of unit failure on forward contracting a generating unit can sell its production in the pool facing the financial risk inherent to volatile pool prices alternatively it can sell its production through the futures market at comparatively stable but smaller average prices unlike the pool forward contracts involve an obligation to sell during a specified time period and if the unit fails it should buy energy from the pool to meet its futures market commitments hence taking into account its forced outage rate the unit should determine the appropriate mix of pool and futures market involvement so that its expected profit is maximized for a prespecified risk level on profit variability within this context this paper analyzes the impact of the degree of unavailability of the generating unit on its forward contracting decisions a detailed case study illustrates the analysis performed

electricity price short term forecasting using artificial neural networks this paper presents the system marginal price smp short term forecasting implementation using the artificial neural networks ann computing technique the described approach uses the three layered ann paradigm with backpropagation the retrospective smp real world data acquired from the deregulated victorian power system was used for training and testing the ann the results presented in this paper confirm considerable value of the ann based approach in forecasting the smp

energy clearing price prediction and confidence interval estimation with cascaded neural networks the energy market clearing prices mcps in deregulated power markets are volatile good mcp prediction and its confidence interval estimation will help utilities and independent power producers submit effective bids with low risks mcp prediction however is difficult since bidding strategies used by participants are complicated and various uncertainties interact in an intricate way furthermore mcp predictors usually have a cascaded structure as several key input factors need to be predicted first cascaded structures are widely used however they have not been adequately investigated this paper analyzes the uncertainties involved in a cascaded neural network nn structure for mcp prediction and develops the prediction distribution under the bayesian framework a computationally efficient algorithm to evaluate the confidence intervals by using the memoryless quasi newton method is also developed testing results on a classroom problem and on new england mcp prediction show that the method is computationally efficient and provides accurate prediction and confidence coverage the scheme is generic and can be applied to various networks such as multilayer perceptrons and radial basis function networks

an agent based approach with collaboration among agents estimation of wholesale electricity price on pjm and artificial data generated by a mean reverting model abstract this study examines the performance of mais multi agent intelligent simulator equipped with various learning capabilities in addition to the learning capabilities the proposed mais incorporates collaboration among agents the proposed mais is applied to estimate a dynamic change of wholesale electricity price in pjm pennsylvania new jersey mainland and an artificial data set generated by a mean reverting model using such different types of data sets the methodological validity of mais is confirmed by comparing it with other well known alternatives in computer science this study finds that the mais needs to incorporate both the mean reverting model and the collaboration behavior among agents in order to enhance its estimation capability the mais discussed in this study will provide research on energy economics with a new numerical capability that can investigate a dynamic change of not only wholesale electricity price but also speculation and learning process of traders

day ahead electricity price forecasting in a grid environment accurate electricity price forecasting is critical to market participants in wholesale electricity markets market participants rely on price forecasts to decide their bidding strategies allocate assets negotiate bilateral contracts hedge risks and plan facility investments market operators can also use electricity price forecasts to predict market power indexes for the purpose of monitoring participants behaviors various forecasting techniques are applied to different time horizons for electricity price forecasting in locational marginal pricing lmp spot markets available correlated data also have to be selected to improve the short term forecasting performance in this paper fuzzy inference system fis least squares estimation lse and the combination of fis and lse are proposed based on extensive testing with various techniques lse provides the most accurate results and fis which is also highly accurate provides transparency and interpretability

multimarket optimal bidding for a power producer this paper considers a profit maximizing thermal producer that participates in a sequence of spot markets namely day ahead automatic generation control agc and balancing markets the producer behaves as a price taker in both the day ahead market and the agc market but as a potential price maker in the volatile balancing market the paper provides a stochastic programming methodology to determine the optimal bidding strategies for the day ahead market uncertainty sources include prices for the day ahead and agc markets and balancing market linear price variations with the production of the thermal producer results from a realistic case study are reported and analyzed conclusions are duly drawn

asymmetry imperfectly transferable utility and the role of fiat money in improving terms of trade abstract we modify the kiyotaki and wright 1991 j economic theory 53 215 235 1993 amer econom rev 83 63 77 framework so that there is a universal double coincidence of wants in all barter matches we also introduce divisible service sidepayments into the model and allow agents to bargain over bundles of goods services and money in bilateral matches in asymmetric matches the agent that values the other s good more dearly will typically have to make a substantial service sidepayment to complete the bargain when sidepayments transfer utility imperfectly the general equilibrium is inefficient agents barter too much when barter is inefficient a robust monetary equilibrium may exist which improves welfare both robust monetary equilibria and welfare improving monetary equilibria require asymmetric matches imperfectly transferable utility and monetary exchange yields better expected terms of trade than barter in contrast to other search models money does not speed up trade

optimal response of a thermal unit to an electricity spot market this paper addresses the optimal response of a thermal unit to an electricity spot market the objective is to maximize the unit profit from selling both energy and spinning reserve in the spot market the paper proposes a 0 1 mixed integer linear programming approach that allows a rigorous modeling of i nonconvex and nondifferentiable operating costs ii exponential start up costs iii available spinning reserve taking into account ramp rate restrictions and iv minimum up and down time constraints this approach overcomes the modeling limitations of dynamic programming approaches and is computationally efficient results from realistic case studies are reported

day ahead electricity price forecasting using the wavelet transform and arima models this paper proposes a novel technique to forecast day ahead electricity prices based on the wavelet transform and arima models the historical and usually ill behaved price series is decomposed using the wavelet transform in a set of better behaved constitutive series then the future values of these constitutive series are forecast using properly fitted arima models in turn the arima forecasts allow through the inverse wavelet transform reconstructing the future behavior of the price series and therefore to forecast prices results from the electricity market of mainland spain in year 2002 are reported