Prediction of Residential Building Demand Response Potential Using Data-Driven Techniques

Abstract

This paper is concerned with the evaluation of the ability of data-driven predictive models to capture the demand response potential in residential buildings. A mid-floor apartment with an air to water heat pump for space heating, utilised as an archetype dwelling, is simulated using EnergyPlus. The research is focused on forecasting the electrical demand from the heating load for the coldest month of the year, considering two types of DR events, load reduction and load increase. After the generation of the synthetic database, an artificial neural network model and a support vector machine model are examined regarding their ability to predict the electrical demand from heating loads.