Electricity Price Forecast using Meteorology data: A study in Australian Energy Market Electricity price as a fundamental cost for each family which is an essential segment in the electricity market. The adjustment of electricity price can present the change in electricity supply and demand relationship. For the electricity supply companies, an appropriate defined electricity price can eventually determine the level of profit. On the other hand, an accurate prediction can help to seize opportunities in the electricity market. In this paper, we aim to predict the electricity price with more confident accuracy by leveraging data mining techniques. Our experiment on 12 months of electricity prices as well as climate data in the New South Wales has achieved a promising prediction result.

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.