The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage. The ability to forecast consumption at different locations in distribution systems will be a key capability of Smart Grids. The goal of this paper is to benchmark state-of-the-art methods for forecasting electricity demand on the household level across different granularities and time scales in an explorative way, thereby revealing potential shortcomings and find promising directions for future research in this area. We apply a number of forecasting methods including ARIMA, neural networks, and exponential smoothening using several strategies for training data selection, in particular day type and sliding window based strategies. We consider forecasting horizons ranging between 15 minutes and 24 hours. Our evaluation is based on two data sets containing the power usage of individual appliances at second time granularity collected over the course of several months. The results indicate that forecasting accuracy varies significantly depending on the choice of forecasting methods strategy and the parameter configuration. Measured by the Mean Absolute Percentage Error (MAPE), the considered state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Overall, we observed MAPEs in the range between 5 and >100 . The average MAPE for the first data set was 30 , while it was 85 for the other data set. These results show big room for improvement. Based on the identified trends and experiences from our experiments, we contribute a detailed discussion of promising future research.
Demand side management and demand response receive increasing attention by research and industry. The research work published so far includes a variety of directions from direct load control or targeted customer interaction to indirect incentive-based control (see @cite_18 for an overview). In order to help balancing demand and supply, demand side management programs require accurate predictions of consumer demand.
@cite_18
2062912550
This paper mainly focuses on demand side management and demand response, including drivers and benefits, shiftable load scheduling methods and peak shaving techniques. Demand side management techniques found in literature are overviewed and a novel electricity demand control technique using real-time pricing is proposed. Currently users have no means to change their power consumption to benefit the whole system. The proposed method consists of modern system identification and control that would enable user side load control. This would potentially balance demand side with supply side more effectively and would also reduce peak demand and make the whole system more efficient. Abstract The smart grid, as an emerging cyber-physical system, is attractive because of features such as distributed energy control and robust load fluctuation management. The demand Response (DR) component in smart grids helps in maintaining demand-supply balance and in controlling consumer side electricity bills. One of the visions of smart grids is to have communication between consumers and suppliers facilitate certain types of DR strategies, e.g., demand bidding (DR-DB). DR-DB is an incentive-based DR, where certain incentives are awarded to consumers who participate in DR events. However, privacy and security in the DR-DB bidding process are of paramount importance as potentially sensitive consumer data is used during the process. In this paper, we propose a private and secure bidding protocol for incentive-based demand-response systems using cryptographic primitives without assuming any trusted third-party. We analyze the security and privacy guarantees; and show that the various stages in our proposed demand bidding are computationally feasible, in terms of cryptographic primitives, through performance evaluation on three different computing environments. The stringent requirement of the demand-supply equilibrium for delivering electricity has traditionally been dealt with a supply-side perspective, assuming that the demand is not alterable. With the promises of the smart grid, demand-side management techniques are increasingly becoming more feasible. A demand-side management technique, called demand response (DR), aims at inducing changes in electricity load in response to financial incentives, some of which involve bidding as the underlying facilitator. It is well-established that the effectiveness of the DR is proportional to the number of participants. Yet, many of the DR programs, including those involving bidding, may suffer due to consumer privacy concerns. Within this context, in this paper, we propose a distributed and multi-unit privacy guaranteeing bidding mechanism as part of a DR program without relying on any third party, trusted or not, to protect the participants’ bidding information, except obviously for the winning price and the winner exposed to the utility. To the best of our knowledge, this is the first such approach for the DR bidding programs. We provide a security analysis of our approach under the honest-but-curious and active adversary assumptions and prove the privacy assuring property. Demand-side energy management improves robustness and efficiency in Smart Grids. Load-adjustment and load-shifting are performed to match demand to available supply. These operations come at a discomfort cost for consumers as their lifestyle is influenced when they adjust or shift in time their demand. Performance of demand-side energy management mainly concerns how robustness is maximized or discomfort is minimized. However, measuring and controlling the distribution of discomfort as perceived between different consumers provides an enriched notion of fairness in demand-side energy management that is missing in current approaches. This paper defines unfairness in demand-side energy management and shows how unfairness is measurable and controllable by software agents that plan energy demand in a decentralized fashion. Experimental evaluation using real demand and survey data from two operational Smart Grid projects confirms these findings. © 2014 IEEE.