Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers

The rapid development of distributed generators and demand response management programs are transforming the traditional consumers to emerging prosumers. While, it is difficult to manage these prosumers because different types of energy are locally generated and consumed with the autonomous operations. For this purpose, this paper proposes a multi-energy forecasting framework based on deep learning methodology to simultaneously predict the electrical, thermal and gas net load of integrated local energy systems. First, the inherent multi-energy load and generation features of heterogeneous prosumers are qualitatively analyzed, and a hierarchical clustering framework is formulated to classify these prosumers into various aggregations to facilitate the multi-energy forecasting model. Then, a deep belief network based forecasting method is developed to extract the hidden features in multi-energy time series, thereby achieving the net-load prediction of numerous prosumers. Finally, the proposed multi-energy net load forecasting methodology is extensively and comprehensively validated using the real data from household-scale prosumers. The comparative results demonstrate the superiority and high forecast accuracy of the proposed methodology, and confirm its capability to cope with the multi-prosumer prediction problem with multi-energy carriers.