Residential buildings can actively participate in energy management strategies by integrating advanced metering infrastructure to have a reliable and stable distribution system. This paper introduces a novel home energy management system (HEMS) framework to minimize total electricity costs by optimizing the charging of an electric vehicle (EV). The methodology incorporates a non-intrusive load monitoring algorithm to extract the EV information from the total power consumption. To ensure accuracy in planning, there is a need for an accurate prediction model for both total power and EV charging profiles. Therefore, deep learning methods are used together with signal processing techniques to accurately predict aggregated power consumption and decompose the EV from it. The effectiveness of the proposed HEMS algorithm is validated using a dataset collected from Northern Norway. The proposed methodology is validated by the test dataset. The implementation of HEMS resulted in a decrease of 15.4% in the total cost of electricity consumption and a significant reduction of 52.2% for charging the EV compared to the base case. Furthermore, the peak of total power is reduced by 23.19% with the adoption of HEMS.

Identifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including discrete wavelet transform, low-pass filter, and seasonality decomposition, to find the best filtering method for disaggregating different flexible loads (e.g., heat pumps). Experimental results are presented for estimating the electricity consumption of a heat pump, a refrigerator, and a dishwasher from the total power of a residential house in British Columbia (a publicly available use case). The results show that AEFLSTM can reduce the loss error (mean absolute error) by 57.4%, 44%, and 55.5% for estimating the power consumption of the heat pump, refrigerator, and dishwasher, respectively, compared to the stand-alone LSTM model. The proposed approach is used for another dataset containing measurements of an electric vehicle to further support the validity of the method. AEFLSTM is able to improve the result for disaggregating an electric vehicle by 22.5%.