A Data-Driven Home Energy Scheduling Strategy Under the Uncertainty in Photovoltaic Generations

To address the uncertainty in photovoltaic (PV) outputs for day-ahead home energy scheduling in hourly timescale, a novel stochastic optimization strategy based data-driven method is proposed. Based on available historical PV outputs, the Gaussian mixture model (GMM) algorithm combined with improved prediction strength of clustering method is applied to establish the forecasted probabilistic PV outputs model. Based on the seven-step approximation model of Gaussian distribution, only PV outputs with larger probability level at each hour are used to generate scenarios. Then the typical scenario set can be constructed by scenario reduction method. By finding the solution in typical scenario set using mixed-integer nonlinear programming (MINLP), the scheduling strategy will be closer to real cases. Test results verify the effectiveness of proposed probabilistic PV output model and solution method.