Feature and model selection for day-ahead electricity-load forecasting in residential buildings

The need for accurate balancing in electricity markets and a larger integration of renewable sources of electricity require accurate forecasts of electricity loads in residential buildings. In this paper, we consider the problem of short-term (one-day ahead) forecasting of the electricity-load consumption in residential buildings. In order to generate such forecasts, historical electricity consumption data are used, presented in the form of a time series with a fixed time step. Initially, we review standard forecasting methodologies including naive persistence models, auto-regressive-based models (e.g., AR and SARIMA), and the triple exponential smoothing Holt-Winters (HW) model. We then introduce three forecasting models, namely i) the Persistence-based Auto-regressive (PAR) model, ii) the Seasonal Persistence-based Regressive (SPR) model, and iii) the Seasonal Persistence-based Neural Network (SPNN) model. Given that the accuracy of a forecasting model may vary during the year, and the fact that models may differ with respect to their training times, we also investigate different variations of ensemble models (i.e., mixtures of the previously considered models) and adaptive model switching strategies. Finally, we demonstrate through simulations the forecasting accuracy of all considered forecasting models validated on real-world data generated from four residential buildings. Through an extensive series of evaluation tests, it is shown that the proposed SPR forecasting model can attain approximately a 7% forecast error reduction over standard techniques (e.g., SARIMA and HW). Furthermore, when models have not been sufficiently trained, ensemble models based on a weighted average forecaster can provide approximately a further 4% forecast error reduction. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper examines short-term forecasting of electricity-load consumption in residential buildings, introducing three new forecasting models and exploring ensemble models and adaptive model switching strategies. Simulation results on validated data demonstrate the superiority of the SPR forecasting model in reducing forecast errors compared to standard techniques.