Improving Net Energy Metering (NEM) Actual Load Prediction Accuracy using an Adaptive Learning Rate LSTM Model for Residential Use Case

¹ College of Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia
² Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia
³ Institute of Power Engineering IPE, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia
⁴ Faculty of Engineering and Built Environment (FETBE), UCSI University, 56000 Kuala Lumpur, Malaysia

^* Corresponding author: sankar@uniten.edu.my

Abstract

As an effort to promote renewable energy-based power generation, one of Malaysia’s initiatives is the net-energy metering (NEM) scheme. One of the shortcomings of residential Photovoltaic (PV) systems under the NEM scheme is that it operates with smart meters only whereby the actual load profiles by the residential consumers remain unknown. Accurate load prediction for NEM consumers is crucial for optimizing energy consumption and effectively managing net metering credits. This study proposes a new model that incorporates an adaptive learning rate and Long Short-Term Memory (LSTM) to predict the solar output power that subsequently predicts the actual load used by the NEM residential consumers. The proposed model is trained and tested using historical time series data of projected PV power and weather conditions, considering the GPS location of the PV system. The outcome of the proposed model is then compared with other state-of-the-art models like ARIMA and regression methods. It is shown that the proposed model outperforms the traditional forecasting models with a Root Mean Square Error (RMSE) value of 0.1942.