Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Abstract

Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.

Weather Forecast Control for Heating of Multi-Family Buildings in Comparison with Feedback and Feedforward Control

“…While most, including the present, articles utilize conventional weather forecast services, Willegas-Mier et al [24] studied a more innovative approach where a fisheye camera was used to track the movements of clouds to predict solar irradiance. In contrast to energy savings and improved thermal comfort, Hedegaard et al [25] studied another advantage of MPC.…”