2.2. Consumption and micro-generation short-term forecasting

Regression is seen as part of the first generation of consumption (load) forecasting methods. It is one of most widely used statistical methods due to its undoubtable advantages such as simplicity and transparency. For electricity load forecasting, regression methods are usually applied to effectively model the relationship of consumption level and other factors such as weather (i.e. temperature, humidity, etc.), day type (workdays and holidays) and consumers profiles.

Several methods based on regression have been used for short-term load forecasting with different levels of success such as ARMAX models [12], multiple regression [13, 14] and regression with neural networks [15–17].

In Ref. [18], authors describe several regression models for the next day peak forecasting. Their models incorporate deterministic influences such as weekend days, stochastic influences such as historical loads, and exogenous factors influences such as temperature. In papers [19–22], authors described other applications of regression models to load forecasting.

According to [23, 24], ARIMA models have proven appropriate for forecasting electricity consumption.

In Section 3.2, we proposed a method based on autoregressive neural networks for short-term forecasting the electricity consumption aggregated at supplier's level for a typical day of the week. The forecasting method is applied on each profile previously determined by SOM. Also, we considered ARIMA method for load forecasting and at the end of Section 3.2, we compared the results for both methods.

Regarding the micro-generation forecasting (small wind turbines and photovoltaic panels installed at consumers' side), the methods depend on the time interval. For example, stochastic methods (persistence and autoregressive patterns) are recommended in Ref. [25] for very short-term prediction (up to 4–6 hours). In addition, other authors [26] proposed Kalman integrated support vector machine (SVM) method to achieve a 10% accuracy improvement by comparing with artificial neural networks or autoregressive (AR) methods. Also a consistent approach is given by the use of ANNs for short-term generation forecast in case of wind turbines and photovoltaic (PV) panels. Various ANN-based algorithms are described in [27, 28], it is proposed Bayesian Regularization algorithms for forecasting. Also in [29, 30], authors proposed back propagation neural networks based on the optimization of Swarm particles.

Stochastic methods can be successfully used in order to determine the PV generation. The authors of the paper [31] analyzed and compared different models for forecasting and concluded that the accuracy of the ARMA model is better than other models.

In Section 4, we analyzed two methods for PV and small wind turbines generation: stochastic method based on ARIMA and feed-forward ANN. The results are compared and conclusions are drawn at the end of the section.
