The Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency - Polset, Siriwan; Khejan, Pisanurat (Engineering and Technology Horizons, 2024)
The development of the electric meter system described in this article is titled as Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency. The primary objective is to improve household energy management through prediction and alerts regarding energy usage. The system is designed to collect electricity usage data and various environmental parameters from households, and it employs five machine learning models to identify the best model for this purpose. The chosen model, Support Vector Regression (SVR), is used to predict energy consumption. In the research methodology, the system records real-time energy usage data into a CSV file. The predictive features include temperature, number of occupants, house size, and appliance usage. This data is standardized before being used to train the SVR model. After training, the model’s predictions are evaluated using the Root Mean Square Error (RMSE). The experimental results show that the SVR model effectively predicts electricity consumption, with a normalized RMSE of 57.56 and a cross-validation RMSE of 58.38, indicating the model’s accuracy. The visualizations provide a clear understanding of the overall relationship between actual and predicted values. Household electricity usage prediction enables users to plan energy consumption more efficiently, potentially reducing costs and improving energy efficiency. The development of this system can be applied in various fields, including industry and agriculture, to promote energy conservation and reduce environmental impact.