Development of Energy Saving Technologies for Smart Buildings by Using Computer Algebra
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velopment of Energy Saving Technologies for Smart Buildings by Using Computer Algebra E. Yu. Shchetinin Financial University under the Government of the Russian Federation, Leningradskii pr. 49, Moscow, 125993 Russia e-mail: [email protected] Received July 10, 2019; revised September 15, 2019; accepted October 3, 2019
Abstract—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 energyefficient 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. DOI: 10.1134/S0361768820050084
1. INTRODUCTION One of the most important directions for economic development is improving the energy efficiency of the manufacturing and consumer sectors of the economy. In this connection, the state program of the Russian Federation “Energy saving and improving energy efficiency for the period up to 2030" was approved. To reduce the environmental impact and costs associated with the commercial building sector, several energy efficient programs were implemented. For instance, at the state and federal levels in Russia, a number of longterm energy saving targets to be achieved through the energy efficient programs were set. Energy efficiency analysis is crucial in setting energy tariffs for building owners, utility payers, and service providers [1–3]. The development of intelligent networks in manufacturing, finance, and services creates new prospects for the development and application of efficient machine learning and data analysis methods, as well as the design of new control modules for cyber-physical energy systems. The introduction of smart meters is beneficial for end users, energy suppliers, and network operators because these devices can provide consumers with near-real-time data that can help them control their actual energy consumption, save money, and reduce greenhouse gas emissions. In addition, smart meters facilitate planning and operation of distribu-
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