Sensitivity analysis of data-driven building energy demand forecasts

Data-driven models of buildings could potentially reduce implementation barriers for demand forecasting and predictive control in the built environment. However, such models appear to be sensitive to the quality of the available input data. Here, we investigate the influence of sampling time, noise level and amount of available measurement data as well as the quality of the weather forecast on a heating demand forecast with online corrected Artificial Neural Networks. Based on a case study, we demonstrate that sampling time has a stronger influence on the prediction performance than noise level and the amount of available data. Furthermore, we show that using measured ambient temperatures for training appears to provide no benefit compared to using weather forecasts.