An LSTM-SAE-Based Behind-the-Meter Load Forecasting Method

Nowadays, modern technologies in power systems have been attracting more attention, and households can supply a portion of or all of their electricity based on on-site generation at their location. This can be challenging for utilities in terms of monitoring and recording the data because the households' facilities can generate or consume the energy without passing it through a meter, increasing the complexity of a distribution network. The speed of transferring data to utilities is another important concern. There is a necessity to send the smart meter (SM) data of each house to a distribution management system (DMS) for more analysis in the shortest possible time. This paper presents a novel deep learning framework collaborating with sequence-to-sequence (seq2seq), long short-term memory (LSTM), and stacked autoencoders (SAEs) to forecast residential load profiles considering the photovoltaic (PV), battery energy storage system (BESS), and electric vehicle (EV) loads with more capability based on pre-defined patterns. Experimental results show that the proposed method achieves outstanding performance in the forecasting process of residential load profiles in comparison with other algorithms. Also, a smart distribution transformer can help utilities to receive the data instantly via wireless communication, which can reduce the transfer duration to every minute and make the prediction and monitoring more manageable considering the different combinations of distributed energy resources (DERs) in residential locations.