A novel similar-day based probability density forecasting framework for residential loads

Probabilistic load forecasting can provide much richer information about uncertainties in residential loads than point load forecasting, and is gaining increasing attention in building efficient and economical demand response programs. However, obtaining satisfactory forecasting results for residential loads is challenging due to their intrinsic nonlinearity and volatility. Furthermore, existing probabilistic forecasting methods are limited in their ability to directly generate probability density forecasting in an efficient and accurate way. To address these issues, a new probability density forecasting framework for residential loads is proposed in this paper. Firstly, a similar-day choosing method is proposed by combining weighted external features and internal load curves to build a similar-day dataset. Then, point forecasting is achieved by training an exclusive predictor on the cor-responding similar-day dataset for each forecasting target. Finally, probability density forecasting is realized by conducting kernel density estimation on the similar-day dataset and calibrating it according to the point fore-casting result. Comparisons with other state-of-the-art methods carried out on the HUE dataset demonstrate the efficiency and superiority of the proposed framework in both point and probability density forecasting.

Automated summary

This paper proposes a new probability density forecasting framework for residential load forecasting. The framework combines weighted external features and internal load curves to build a similar-day dataset, and uses an exclusive predictor for point forecasting. Probability density forecasting is achieved through kernel density estimation and calibration. Comparisons with other methods demonstrate the efficiency and superiority of the proposed framework in both point and probability density forecasting.