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Impact of external factors on electricity consumption prediction in office building with photovoltaic supply
Języki publikacji
Abstrakty
Planowanie zużycia energii elektrycznej ze względu na pojawiające się niespodziewanie przerwy w dostawie staje istotnym aspektem zarządzania utrzymania budynków. Analiza szeregów czasowych pozwala na predykowanie zużycia energii elektrycznej w kolejnych latach na podstawie danych historycznych. Celem badania jest weryfikacja wpływu czynników zewnętrznych na predykcję ilości zużycia energii elektrycznej. W badaniach zostały wykorzystane metody analizy szeregów czasowych: model naiwny z sezonowością, regresji liniowej oraz Facebook Prophet. Wyniki pokazują, że zaproponowane modele w zadawalający sposób są w stanie prognozować zapotrzebowanie na energię.
Planning for electricity consumption due to power outages occurring unexpectedly is becoming an important aspect of building maintenance management. Time series analysis makes it possible to predict electricity consumption in future years based on historical data. The purpose of the study is to verify the influence of external factors on the prediction of the amount of electricity consumption. The study used time series analysis methods: naive model with seasonality, linear regression and Facebook Prophet. The results show that the proposed models are able to predict energy demand satisfactorily.
Wydawca
Czasopismo
Rocznik
Tom
Strony
221--226
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
- Akademia Górniczo-Hutnicza, Katedra Automatyki i Robotyki, Al. Mickiewicza 30, 30-150 Kraków
- Tauron Polska Energia S.A.
autor
- Akademia Górniczo-Hutnicza, Katedra Automatyki i Robotyki, Al. Mickiewicza 30, 30-150 Kraków
autor
- Akademia Górniczo-Hutnicza, Katedra Automatyki i Robotyki, Al. Mickiewicza 30, 30-150 Kraków
autor
- Akademia Górniczo-Hutnicza, Katedra Automatyki i Robotyki, Al. Mickiewicza 30, 30-150 Kraków
Bibliografia
- [1] Ray M., Samal P., Kumar Panigrahi C., The influencing factors on efficacy enhancement of HVAC systems – A review, Materials Today: Proceedings, (2021), ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.07.264
- [2] Tsanas A., Xifara A., Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools, Energy and Buildings, Volume 49, (2012), 560- 567, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2012.03.003.
- [3] Yu Z., Haghighat F., Fung B.C.M., Yoshino H., A decision tree method for building energy demand modeling, Energy and Buildings, Volume 42, Issue 10, (2010), 1637-1646, ISSN 0378- 7788, https://doi.org/10.1016/j.enbuild.2010.04.006.
- [4] Dincer I., On thermal energy storage systems and applications in buildings, Energy and Buildings, Volume 34, Issue 4, (2002), 377-388, ISSN 0378-7788, https://doi.org/10.1016/S0378- 7788(01)00126-8.
- [5] Farouk N., Alhumaidi A. Alotaibi, Alshahri A.H., Almitani K. H., Using PCM in buildings to reduce HVAC energy usage taking into account Saudi Arabia climate region, Journal of Building Engineering, Volume 50, (2022), 104073, ISSN 2352-7102, https://doi.org/10.1016/j.jobe.2022.104073.
- [6] Hou J., Li X., Wan H, Sun Q., Dong K., Huang G., Real-time optimal control of HVAC systems: Model accuracy and optimization reward, Journal of Building Engineering, Volume 50, 2022, 104159, ISSN 2352-7102, https://doi.org/10.1016/j.jobe.2022.104159.
- [7] Belafi, Z., Hong, T., Reith, A. Smart building management vs. intuitive human control—Lessons learnt from an office building in Hungary. Build. Simul. 10, 811–828 (2017). https://doi.org/10.1007/s12273-017-0361-4
- [8] Bagheri-Esfeh H., Reza Dehghan M., Multi-objective optimization of setpoint temperature of thermostats in residential buildings, Energy and Buildings, Volume 261, (2022), 111955, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2022.111955.
- [9] Guo, C., Ge, Q., Jiang, H., Yao, G., & Hua, Q. (2020). Maximum power demand prediction using fbprophet with adaptive Kalman filtering. IEEE Access, 8, 19236-19247., Chicago,
- [10] M. Daraghmeh, A. Agarwal, R. Manzano and M. Zaman, "Time Series Forecasting using Facebook Prophet for Cloud Resource Management," 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICCWorkshops50388.2021.9473607.
- [11] Brandt, J. A., & Bessler, D. A. (1983). Price forecasting and evaluation: An application in agriculture. Journal of Forecasting, 2(3), 237-248.
- [12] Lacina, M., Brian Lee, B. and Zhaohui Xu, R. (2011), "An Evaluation of Financial Analysts and Naïve Methods in Forecasting Long-Term Earnings", Lawrence, K.D. and Klimberg, R.K. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 8), Emerald Group Publishing Limited, Bingley, pp. 77-101.
- [13] De Felice, M., Alessandri, A., & Ruti, P. M. (2013). Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models. Electric Power Systems Research, 104, 71-79.
- [14] Jan F, Shah I, Ali S. Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis. Energies. 2022; 15(9):3423. https://doi.org/10.3390/en15093423
- [15] Nogales, F. J., & Conejo, A. J. (2006). Electricity price forecasting through transfer function models. Journal of the Operational Research Society, 57(4), 350-356.
- [16] Svec, J., & Stevenson, M. (2007). Modelling and forecasting temperature based weather derivatives. Global Finance Journal, 18(2), 185-204., Chicago,
- [17] Mohamed, Z., & Bodger, P. (2005). Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy, 30(10), 1833-1843.
- [18] Ng, S. T., Skitmore, M., & Wong, K. F. (2008). Using genetic algorithms and linear regression analysis for private housing demand forecast. Building and Environment, 43(6), 1171-1184.
- [19] Marill, K. A. (2004). Advanced statistics: linear regression, part II: multiple linear regression. Academic emergency medicine, 11(1), 94-102.
- [20] Salem, O., Guerassimov, A., Mehaoua, A., Marcus, A., & Furht, B. (2014). Anomaly detection in medical wireless sensor networks using SVM and linear regression models. International Journal of E-Health and Medical Communications (IJEHMC), 5(1), 20-45., Chicago,
- [21] . Peng and X. Li, "Application of a multifactor linear regression model for stock portfolio optimization," in 2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) 2018, pp. 367-370
- [22] Zunic, E., Korjenic, K., Hodzic, K., & Donko, D. (2020). Application of facebook's prophet algorithm for successful sales forecasting based on real-world data. arXiv preprint arXiv:2005.07575.
- [23] Soloviev, V., Titov, N., & Smirnova, E. (2020, July). Coking coal railway transportation forecasting using ensembles of ElasticNet, LightGBM, and Facebook prophet. In International Conference on Machine Learning, Optimization, and Data Science (pp. 181-190). Cham: Springer International Publishing.
- [24] Garlapati, A., Krishna, D. R., Garlapati, K., Rahul, U., & Narayanan, G. (2021, April). Stock price prediction using Facebook Prophet and Arima models. In 2021 6th International Conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE.
- [25] Kaninde, S., Mahajan, M., Janghale, A., & Joshi, B. (2022). Stock price prediction using facebook prophet. In ITM Web of Conferences (Vol. 44, p. 03060). EDP Sciences.
- [26] Saiktishna, C., Sumanth, N. S. V., Rao, M. M. S., & Thangakumar, J. (2022, May). Historical Analysis and Time Series Forecasting of Stock Market using FB Prophet. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1846-1851). IEEE.
- [27] Battineni, G., Chintalapudi, N., & Amenta, F. (2020). Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model. Applied Computing and Informatics.
- [28] Satrio, C. B. A., Darmawan, W., Nadia, B. U., & Hanafiah, N. (2021). Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science, 179, 524-532.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a7a474a2-7baa-4aff-8a51-6c24ef1e5552
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