Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

Summary The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy‐saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature‐inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web‐based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy‐saving and carbon emission‐reducing in cities and aid utility companies in establishing energy conservation guidelines. The purpose of this investigation is to assist energy provider in setting the direction of energy conservation policies. Evaluation indices reveal that the forecasting model of energy consumption is accurate and provides effective information. A forecasting system combined with a web crawler runs automatically to provide information on city‐level electricity consumption.