## 4 Technology
### Forecasting  
Forecasting is a key functionality of a smart grid system. Through forecasting, the Grid is able to balance loads, optimize power distribution and handle failures. The main problem of forecasting, in a modern electricity network, is given by the Renewable Energy Sources (RES). The energy produced by RES can vary, and its variation depends on several parameters (climate conditions, source plant location, _etc._) In this sense, their contribution in terms of energy can be difficult to predict, because there are too many variables to observe. In some cases, this unpredictability may become a limitation: since energy providers are unable to cope with massive amounts of unpredictable renewable energy sources, many countries and states impose by law specific limits for customer participation in energy generation. In Massachusetts, for example, this limit is set to 1% of all customers [17]. In this section we will provide some examples of forecasting techniques and algorithms for smart grids.  
[PERSON] _et al._[18] present a solution based upon a central dispatcher with the following functions: short-term forecast of the power produced by renewable energy sources (RES), short-term load forecast and day-ahead load profile prediction, distribution system state estimation, day-ahead economic dispatching and on-line scheduling of the optimal distributed resources' operating conditions. The forecast algorithm was based on a neural network. The results can be seen in Figure 5.  
Another approach is presented by [PERSON] _et al._[17], where the goal is automatic prediction of energy generation from renewable sources using weather forecasts. In their work, authors focus on solar generation. They collected 10 months of weather forecast and solar intensity data, searching for possible correlations. Then they used a machine-learning technique to develop a prediction model for short-term forecasting, specifically three hours ahead. Upon cross-validation, the prediction accuracy of their model as regards solar intensity (from which solar energy production can be calculated) is in the order of 130 W/m\({}^{2}\). They claim this accuracy to be 51% higher with respect to the traditional PPF (Past-Predicts-Future) models where the previous day solar intensity is used to predict the next day values.  
Another dimension of smart grid forecasting techniques regards demand forecasting. This is crucial to energy providers and traders, because as dynamic pricing is enabled, the reaction of the customers to price changes may cause significant shifts in the demand curve. A possible solution to this problem is presented by [PERSON] _et al._[19]. Authors designed a hybrid forecasting framework, based upon two different models: a Multi-Input, Multi-Output (MIMO) engine that generates initial demand and  
Figure 5: Example of forecasted vs. measured global radiation [18].  
price forecasting, and a Data Association Mining (DAM) algorithm to refine the predictions in order to improve the accuracy. The framework is divided into three logical phases: during the first phase, the MIMO engine extracts demand and price forecasting from historical data. Subsequently, in the second phase the DAM algorithm extracts possible rules and patterns in deviation curves of the observed values. In the third phase, these patterns are applied to the initial forecasting, using Fuzzy Inference Systems (FIS) to improve its accuracy. Authors applied their techniques to two different datasets (the actual Australian Market data and simulated New England Market data) to generate 24-hours ahead forecasts. Authors claim an accuracy in the order of 2% for demand forecasting and 10% for price forecasting.