Behind-the-Fence Generation Forecasting: A Batched Decomposition Framework

In this paper, we carry out behind-the-fence (BTF) generation forecasting using a new decomposition framework called batched decomposition framework. Here, BTF is framed as a particular structuring of the behind-the-meter (BTM) problem, where power is produced at generation and industrial facilities for internal loads rather than being supplied directly to the grid. BTF forecast is important for power system operators as it aids planning and decision making. This study employs a novel decomposition framework that effectively manages the non-linearity of BTF data while preventing the information leakage issues commonly found in traditional decomposition approaches. To assess the effectiveness of the proposed batched decomposition framework, we tested it on forecasting 24 hours ahead BTF for two Canadian provinces, Alberta and Quebec. The proposed method demonstrates high forecasting accuracy, comparable to the traditional decomposition method, while also avoiding information leakage and ensuring the practicability of the solution. Additionally, the results of the proposed method is benchmarked against various state of the art models using various error metrics. The batched decomposition method was shown to outperform the benchmarks for both test cases.