IoT-Enabled energy conservation in residential Buildings: Machine learning models for analyzing annual solar power consumption

Abstract

This study investigates enhancing the self-consumption of solar panels by integrating thermal and electrical energy storage using lithium-ion batteries in a duplex building over a year. Machine learning algorithms are applied to understand energy usage patterns. An IoT-connected model is introduced to reduce electricity expenses by boosting consumption with solar energy and extending solar panel lifespan through optimization. The model includes lithium-ion batteries for energy storage and a thermal cooling system to regulate panel temperatures. Data collected from sensors monitoring occupancy, temperature, humidity, energy, and thermostats are sent to a cloud-based platform via IoT. Results show peak monthly energy conservation measures of 41.6 kW and 28 kW during summer for the two residences. Winter witnesses a reduction of 13.9 % and 7 % in energy consumption, while autumn sees an 18 % decrease for one residence and stability for the other. Real-time IoT monitoring aids model training, with the Gaussian Probability model emerging as the most accurate. Optimization analysis underscores the thermal cooling system’s efficacy in improving solar panel performance and reducing electricity costs. Integration of machine learning and IoT curtails energy consumption to 22 kWh and 15.7 kWh for the respective residences, offering inhabitants free energy from renewable sources and ensuring swift recovery of solar panel installation costs.