ABSTRACT
With increasing energy consumption, advanced technologies are being developed to save energy by managing demand through electronic equipment legislation. Artificial intelligence, particularly machine learning, is crucial for better energy consumption solutions and home energy management systems. Predictive models like support vector regression (SVR) and backpropagation artificial neural network (BP-ANN) accurately predict energy demand and handle complex data sets. The multi-layered architecture of BP-ANN identifies hidden patterns in energy consumption, aiding in forecasting electricity supply to meet demand. This approach uses training data on time, equipment requirements, intensity, and duration to provide accurate energy-saving solutions.
