Enhancing building energy consumption prediction using LSTM, Kalman filter, and continuous wavelet transform

In order to enhance grid stability, facilitate efficient power supply planning, and minimize energy wastage, precise prediction of building energy usage stands as a significant worldwide issue. This paper proposes a prediction model based on multi-layer Long Short-Term Memory (LSTM) coupled with data denoising techniques, including the continuous wavelet transform (CWT) and Kalman filter (KF) to attain superior prediction performance. The primary goal of KF and CWT for time series data is denoising the data to enhance the signal and remove noise, making it easier to identify and isolate relevant patterns. The results from a comparative study focusing on urban household electricity consumption in Morocco with different step sizes, along with five extended applications, demonstrate that the proposed model outperforms basic LSTM and other existing models. The Mean Absolute Percentage Error (MAPE) for the comparative study with different step sizes stands at 4.02 % and 2.95 % respectively. Additionally, the MAPE for the five extended applications across different buildings are 0.88 %, 3.89 %, 0.69 %, 0.89 %, 0.87 %, respectively. These results highlight the superior accuracy of the proposed model across all metrics, making it a promising technique for predicting building energy consumption. This accuracy is paramount for optimizing energy management strategies, enabling informed decision-making and resource allocation in pursuit of sustainable and efficient building operations.