Predicting Energy Consumption and Time of Use of Home Appliances in an HEMS Using LSTM Networks and Smart Meters: A Case Study in Sincelejo, Colombia

Rising household electricity consumption, driven by technological advances and increased indoor activity, has led to higher energy costs and an increased reliance on non-renewable sources, exacerbating the carbon footprint. Home energy management systems (HEMS) are positioning themselves as an efficient alternative by integrating artificial intelligence to improve their accuracy. Predictive algorithms that provide accurate data on the future behavior of energy consumption and appliance usage time are required in these HEMS to achieve this goal. This study presents a predictive model based on recurrent neural networks with long short-term memory (LSTM), known to capture nonlinear relationships and long-term dependencies in time series data. The model predicts individual and total household energy consumption and appliance usage time. Training data were collected for 12 months from an HEMS installed in a typical Colombian house, using smart meters developed in this research. The model’s performance is evaluated using the mean squared error (MSE), reaching a value of 0.0168 kWh2. The results confirm the effectiveness of HEMS and demonstrate that the integration of LSTM-based predictive models can significantly improve energy efficiency and optimize household energy consumption.