Energy Consumption Forecasting and Optimization in Smart HVAC Systems Using Deep Learning

This research details a method for predicting power usage that makes use of deep learning (DL) techniques, namely Bidirectional LSTM (BiLSTM) and Long Short-Term Memory (LSTM) models. For both the training and evaluation of the models, a real-world dataset was utilized, which included the hourly electricity usage of a Phoenix, USA, hospital building. Effective learning of temporal patterns was made possible by preprocessing, normalizing, and segmenting the data into sequences. Both LSTM and BiLSTM networks were developed and trained to perform 24-hour (short-term), 7-day (medium-term), and monthly (long-term) electricity consumption forecasting. A recursive multi-step prediction strategy was employed for extended forecasting horizons. They employed such measures of industry standards as MSE and Root RMSE to analyze prediction’s accuracy. Based on the findings, BiLSTM is superior to LSTM in the area of capturing complex consumption patterns, indicating that the former can be deployed to enhance energy control and optimization of smart HVAC systems and their energy management and planning