Efficient Multivariate LSTM Forecasting of HVAC Energy Consumption in IoT-Enabled Smart Buildings

Abstract

HVAC systems use up about half of the total energy in smart buildings and are a key focus of optimization. The demand of HVAC energy is very difficult to forecast with high accuracy due to the nonlinear nature of HVAC operations, high temporal variability, and interdependencies among environmental and operational variables. Traditional forecasting methods like regression based models and ARIMA often do not reflect such multivariate dependencies resulting in incompetent energy management. This paper presents a multivariate Long Short-Term Memory (LSTM) model that will be developed to learn the long-term temporal dynamics of various variables related to HVAC. The model is trained and tested on a real-world benchmark dataset, which includes 11 sensor-derived features, and uses one fully connected LSTM layer with 50 hidden units trained using the Adam algorithm. Root Mean Square Error (RMSE) and the coefficient of determination (R2) are reported per variable as measures of forecast performance. The experimental findings indicate that the model is accurate, over 90% on most variables, a fact that justifies the fact that the model is effective in overcoming the weaknesses of the traditional methods and giving accurate predictions that can be incorporated into smart building energy management systems. Further research will focus on hybrid deep learning networks and TinyML networks to run on edge devices that are IoT-enabled.