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18 | 05 | 2024
10.14489/vkit.2020.08.pp.029-037

DOI: 10.14489/vkit.2020.08.pp.029-037

Щетинин Е. Ю.
ИССЛЕДОВАНИЕ ЭНЕРГОСБЕРЕГАЮЩИХ ТЕХНОЛОГИЙ В ЭЛЕКТРОСНАБЖЕНИИ УМНЫХ ЗДАНИЙ С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА
(с. 29-37)

Аннотация. На основе модели градиентного бустинга предложен метод моделирования и прогнозирования энергопотребления комплекса зданий, а также разработаны реализующие его компьютерные алгоритмы. Для оценки эффективности применения метода были использованы данные об энергопотреблении 300 коммерческих зданий. Результаты компьютерного моделирования показали, что использование разработанных алгоритмов повысило точность прогнозирования более чем в 80 % случаев по сравнению с другими алгоритмами машинного обучения.

Ключевые слова:  энергопотребление; умные здания; машинное обучение; случайный лес; градиентный бустинг.

 

Shchetinin E. Yu.
ENERGY SAVING TECHNOLOGIES IN POWER SUPPLY OF SMART BUILDINGS USING ARTIFICIAL INTELLIGENCE
(pp. 29-37)

Abstract. Intelligent energy saving and energy efficiency technologies are the modern large-scale global trend in the energy systems development. The demand for smart buildings is growing not only in the world, but also in Russia, especially in the market of construction and operation of large business centers, shopping centers and other business projects. Accurate cost estimates are important for promoting energy efficiency construction projects and demonstrating their economic attractiveness. The growing number of digital measurement infrastructure, used in commercial buildings, led to increase access to high-frequency data that can be used for anomaly detection and diagnostics of equipment, heating, ventilation, and optimization of air conditioning. This led to the use of modern and efficient machine learning methods that provide promising opportunities to obtain more accurate forecasts of energy consumption of the buildings, and thus increase energy efficiency. In this paper, based on the gradient boosting model, a method of modeling and forecasting the energy consumption of buildings is proposed and computer algorithms are developed to implement it. Energy consumption dataset of 300 commercial buildings was used to assess the effectiveness of the proposed algorithms. Computer simulations showed that the use of these algorithms has increased the accuracy of the prediction of energy consumptionin more than 80 percent of cases compared to other machine learning algorithms.

Keywords: Energy consumption; Smart buildings; Machine learning; Random forest; Gradient boosting.

Рус

Е. Ю. Щетинин (Департамент математики Финансового университета при Правительстве Российской Федерации, Москва, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Eng

E. Yu. Shchetinin (Financial University under the Government of the Russian Federation, Mathematics Department, Moscow, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript  

Рус

1. Gellings C. W. The Smart Grid: Enabling Energy Efficiency and Demand Response. The Fairmont Press, Inc., 2009.
2. Arghira N., Hawarah L., Ploix S., Jacomino M. Prediction of Appliances Energy Use in Smart Homes // Energy. 2012. V. 48, No. 1. P. 128 – 134.
3. Breiman L. Random Forests // Machine Learning. 2001. V. 45, No. 1. P. 5 – 32.
4. Shchetinin E. Yu., Melezhik V. S., Sevastyanov L. A. Improving the Energy Efficiency of the Smart Buildings with the Boosting Algorithms // Proceedings of the Selected Papers of the 12th International Workshop on Applied Problems in Theory of Probabilities and Mathematical Statistics in the framework of the Conference on Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems (APTP+MS'2018). Lisbon, Portugal, 22 – 27 October 2018. CEUR Workshop Proceedings. V. 2332. P. 69 – 78.
5. Ediger V., Akar S. ARIMA Forecasting of Primary Energy Demand by Fuel in Turkey // Energy Policy. 2007. V. 35. P. 1701 – 1708.
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8. Shchetinin E. Yu. Modeling the Energy Consumption of Smart Buildings Using Artificial Intelligence // Proceedings of the Selected Papers of the 9th International Conference Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems (ITTMM 2019). Peoples’ Friendship University of Russia (RUDN University) Moscow; Russian Federation, 15 – 19 April 2019. CEUR Workshop Proceedings. V. 2407. P. 130 – 140.
9. Burnham K. P., Anderson D. R. Model Selection and Multi-Model Inference: A Practical, Information-Theoretic Approach, Springer Verlag, 2002.
10. Granderson J., Touzani S., Custodio C. Accuracy of Automated Measurement and Verification Techniques for Energy Savings in Commercial Buildings // Applied Energy. 2016. No. 173. P. 296 – 308.
11. Ireland’s Open Data Portal [Электронный ресурс]. URL: https://data.gov.ie/dataset/energy-consumption-gas-and-electricity-civic-offices-2009-2012/resource/ (дата обращения: 18.02.2020).
12. Shchetinin E. Yu. Cluster-based Energy Consumption Forecasting in Smart Grids / Springer (CCIS). 2018. Springer. V. 919. P. 446 – 456.

Eng

1. Gellings C. W. (2009). The Smart Grid: Enabling Energy Efficiency and Demand Response. The Fairmont Press, Incorporated.
2. Arghira N., Hawarah L., Ploix S., Jacomino M. (2012). Prediction of Appliances Energy Use in Smart Homes. Energy, Vol. 48, (1), pp. 128 – 134.
3. Breiman L. (2001). Random Forests. Machine Learning, Vol. 45, (1), pp. 5 – 32.
4. Shchetinin E. Yu., Melezhik V. S., Sevastyanov L. A. (2018). Improving the Energy Efficiency of the Smart Buildings with the Boosting Algorithms. Proceedings of the Selected Papers of the 12th International Workshop on Applied Problems in Theory of Probabilities and Mathematical Statistics in the framework of the Conference on Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems (APTP+MS'2018). CEUR Workshop Proceedings, Vol. 2332, pp. 69 – 78. Lisbon.
5. Ediger V., Akar S. (2007). ARIMA Forecasting of Primary Energy Demand by Fuel in Turkey. Energy Policy, Vol. 35, pp. 1701 – 1708.
6. Cincotti S., Gallo G., Ponta L. (2014). Modeling and Forecasting of Electricity Spot-Prices: Computational Intelligence vs. Classical Econometrics. AI Communications, Vol. 27, pp. 301 – 314.
7. Ardakani F. J., Ardehali M. M. (2014). Novel Effects of Demand Side Management Data on Accuracy of Electrical Energy Consumption Modeling and Longterm Forecasting. Energy Conversion Management, 78, pp. 745 – 752.
8. Shchetinin E. Yu. (2019). Modeling the Energy Consumption of Smart Buildings Using Artificial Intelligence. Proceedings of the Selected Papers of the 9th International Conference Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems (ITTMM 2019). Peoples’ Friendship University of Russia (RUDN University). CEUR Workshop Proceedings, Vol. 2407, pp. 130 – 140. Moscow.
9. Burnham K. P., Anderson, D. R. (2002). Model Selection and Multi-Model Inference: A Practical, Information-Theoretic Approach, Springer Verlag.
10. Granderson J., Touzani S., Custodio C. (2016). Accuracy of Automated Measurement and Verification Techniques for Energy Savings in Commercial Buildings. Applied Energy, 173, pp. 296 – 308.
11. Ireland’s Open Data Portal. Available at: https://data.gov.ie/dataset/energy-consumption-gas-and-electricity-civic-offices-2009-2012/resource/ (Accessed: 18.02.2020).
12. Shchetinin E. Yu. (2018). Cluster-based Energy Consumption Forecasting in Smart Grids / Springer (CCIS). Springer, Vol. 919, pp. 446 – 456.

Рус

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