Day ahead electricity price forecast by NARX model with LASSO based features selection The availability of accurate dayahead price forecasts is crucial to achieve an effective participation to electricity markets. Starting from available state of the art, we propose a forecast technique exploiting a nonlinear auto regressive model with exogenous input, including a feature selection mechanism based on the Least Absolute Shrinkage and Selection Operator (LASSO). The rationale behind such a choice is twofold. On the one hand, we aim to target potential increase of forecast accuracy by learning complex nonlinear mappings. On the other hand, we want to increase the interpretability of the resulting model and minimize the effort needed to properly set up the forecaster. A framework such as the LASSO, capable to selfextract features from spot price multivariate time series, might represent a very useful tool for industrial practitioners. Experiments have been performed on Italian market dataset, demonstrating that the proposed method can extract useful features and achieve robust performance. Moreover, we show how the proposed method can support interpretation of forecaster structure and it can reveal interesting correlations within the regression set.

ShortTerm Electricity Load and Price Forecasting using Enhanced KNN In this paper, we introduced a new enhanced technique, to resolve the issue of electricity price and load forecasting. In Smart Grids (SGs) Price and load forecasting is the major issue. Framework of enhanced technique comprises of classification and feature engineering. Feature engineering comprises of feature selection and feature extraction. Decision Tree Regression (DTR) is used for feature selection. Recursive Feature Elimination (RFE) is used for feature selection which eliminates the redundancy of features. The second step of feature engineering, feature extraction, is done using Singular Value Decomposition (SVD), which reduces the dimensionality of features. Last step is to predict the load and forecast. For forecasting electricity load and price, two existing techniques, KNearest Neighbors (KNN) and MultiLayer Perceptron (MLP), and a newly proposed technique known as Enhanced KNN (EKNN) is being used. The proposed technique outperforms than MLP and KNN in terms of accuracy. KNN is working on nonparametric method which is used for classification and regression.