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New Evaluation Metrics for Electrical Demand Forecasting: Application to the Residential Sector

This paper describes a methodological approach for the assessment of a short-term load forecasting model based on an Artificial Neural Network (ANN). Applied to the specific context of the residential houses’ electrical demand, the proposed methodology intends to get a clear overview of the power consumptions’ behaviour and consequently a meaningful feedback for improving home energy management within the framework of Demand Response programs. Based first on a detailed load analysis to bring a more comprehensive knowledge of the power demand of a typical household, the approach is then moving towards the selection of the most suitable ANN architecture to achieve forecasting aims. The deployment of the methodology is ongoing with the assessment of the forecasts performance by the definition of two new evaluation metrics. Compared to more conventional ones as reported in the literature, the newly defined indexes in this study are found more sensitive to the forecast error recorded at peak times. The major benefit of the current approach is to provide the way to measure the quality of a predictive model by quantifying its performance for peak load forecasting, which is the focus of any load control actions when designing demand response strategies.