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Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment

Author

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  • Ahmad, Tanveer
  • Chen, Huanxin
  • Huang, Ronggeng
  • Yabin, Guo
  • Wang, Jiangyu
  • Shair, Jan
  • Azeem Akram, Hafiz Muhammad
  • Hassnain Mohsan, Syed Agha
  • Kazim, Muhammad

Abstract

The substantial measure of energy usage connected to the building atmosphere supports and sustains power usage modeling diligence. Amongst the numerous strategies to elaborate energy methods, supervised based machine learning approaches are immeasurable alternative to circumvent the inconvenience correlated to various engineering and data mining approaches when measured/observed data are accessible. This research depicts an analysis of electricity requirement forecasting by supervised based machine learning models with the limited data information. The power usage or energy consumption data is collected from power transmission and distribution networked organization independent system operator New England for one-year ahead energy forecasting. Moreover, energy consumption data is categorized into monthly, seasonally and yearly basis to foresee the performance for short-term, medium-term and long-term as well. Four-supervised based machine learning models employed for energy forecasting which are: i) Binary Decision Tree; ii) Compact Regression Gaussian Process; iii) Stepwise Gaussian Processes Regression; iv) Generalized Linear Regression Model. The input variables comprise the limited external environmental data, day-type/hour-type and the net energy consumption of various types of load. The output is the total energy demand of the building power usage. Modeling studies are escorted for expected energy demand in future perceptive based on Independent System Operator New England data. The performance evaluation indices applied in evaluating the model's forecasting error are coefficient of variation and mean absolute percentage error. In autumn season, the best MAPE and CV of the binary decision tree is 0.809% and 1.359% respectively for seasonal forecasting, and is 0.989% and 1.601% respectively for yearly forecasting. It is observed that the accuracy in forecasting is modest in the autumn season. In yearly prediction, the MAPE and CV of compact regression Gaussian process, stepwise Gaussian processes regression and generalized linear regression are 3.245% and 3.650%, 4.039% and 4.860%, 5.118% and 5.927% respectively. The machine learning model's performance compared and validated with the actual energy consumption, existing artificial neural network model and the mean absolute percentage error and coefficient of variation found 2.416% and 3.290% respectively for yearly prediction. It is depicted that including the utilization of limited energy usage and environmental data as one of the model's input variables, the electricity forecasting precision is more accurate, precise and can be improved.

Suggested Citation

  • Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
  • Handle: RePEc:eee:energy:v:158:y:2018:i:c:p:17-32
    DOI: 10.1016/j.energy.2018.05.169
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    References listed on IDEAS

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    7. Jeong, Dongyeon & Park, Chiwoo & Ko, Young Myoung, 2021. "Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration," Applied Energy, Elsevier, vol. 282(PB).
    8. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
    9. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
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    14. Li, Pengshun & Zhang, Yi & Zhang, Yi & Zhang, Kai & Jiang, Mengyan, 2021. "The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus," Energy, Elsevier, vol. 218(C).
    15. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
    16. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    17. Peplinski, McKenna & Dilkina, Bistra & Chen, Mo & Silva, Sam J. & Ban-Weiss, George A. & Sanders, Kelly T., 2024. "A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets," Applied Energy, Elsevier, vol. 357(C).
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    19. Melillo, Andreas & Durrer, Roman & Worlitschek, Jörg & Schütz, Philipp, 2020. "First results of remote building characterisation based on smart meter measurement data," Energy, Elsevier, vol. 200(C).
    20. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    21. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    22. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    23. Zhou, Yuekuan & Zheng, Siqian & Zhang, Guoqiang, 2020. "Machine learning-based optimal design of a phase change material integrated renewable system with on-site PV, radiative cooling and hybrid ventilations—study of modelling and application in five clima," Energy, Elsevier, vol. 192(C).
    24. Yang, Yunpeng & Yang, Weixin & Chen, Hongmin & Li, Yin, 2020. "China’s energy whistleblowing and energy supervision policy: An evolutionary game perspective," Energy, Elsevier, vol. 213(C).

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