Development of Energy Saving Technologies for Smart Buildings by Using Computer Algebra

Intelligent energy saving and energy efficient technologies are a modern large-scale global trend in the development of energy systems. Accurate estimates of energy savings are important for promoting energy-efficient construction projects and demonstrating their economic potential. The growing digital measurement infrastructure used in commercial buildings increases the availability of high-frequency data that can be employed for anomaly detection, diagnostics of equipment, heating systems, and ventilation, as well as optimization of air conditioning. This implies the application of modern machine learning methods capable of generating more accurate energy consumption forecasts for buildings to improve their energy efficiency. In this paper, based on the gradient boosting model, a method for modeling and forecasting energy consumption of buildings is proposed and computer algorithms for its software implementation in the SymPy computer algebra system are developed. To assess the efficiency of the proposed algorithms, a dataset that characterizes energy consumption of 300 commercial buildings is used. Results of computer simulations show that these algorithms improve the accuracy of energy consumption forecasts in more than 80% of cases as compared to other machine learning algorithms.