Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Abstract

This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such as weather conditions. Leveraging a dataset that includes electrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and feature engineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and bidirectional gated recurrent unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The study acknowledges the limitations of the dataset, particularly in comparing electricity usage during work hours and outside work hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for greater applicability. The ultimate goal is to develop a predictive model that serves as a valuable tool to improve energy management in smart office settings, optimize electricity usage, and contribute to long-term company profitability.