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Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings

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  1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
  2. Edgar Segovia & Vladimir Vukovic & Tommaso Bragatto, 2021. "Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand," Energies, MDPI, vol. 14(22), pages 1-14, November.
  3. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
  4. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
  5. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
  6. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
  7. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  8. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  9. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
  10. Banafshe Parizad & Hassan Ranjbarzadeh & Ali Jamali & Hamid Khayyam, 2024. "An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price," Sustainability, MDPI, vol. 16(6), pages 1-17, March.
  11. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2020. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm," Energies, MDPI, vol. 13(8), pages 1-20, April.
  12. Fangze Zhou & Hui Zhou & Zhaoyan Li & Kai Zhao, 2022. "Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy," Energies, MDPI, vol. 15(15), pages 1-18, July.
  13. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
  14. Hongling Liu & Chuanyu Bie & Fan Luo & Jianqiang Kang & Yuping Zhang, 2022. "Rapid Prediction of Retired Ni-MH Batteries Capacity Based on Reliable Multi-Parameter Driven Analysis," Energies, MDPI, vol. 15(23), pages 1-11, December.
  15. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
  16. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
  17. Mansoor Khan & Tianqi Liu & Farhan Ullah, 2019. "A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis," Energies, MDPI, vol. 12(12), pages 1-21, June.
  18. Rafael E. Carrillo & Antonis Peppas & Yves Stauffer & Chrysa Politi & Tomasz Gorecki & Pierre-Jean Alet, 2022. "A Multilevel Control Approach to Exploit Local Flexibility in Districts Evaluated under Real Conditions," Energies, MDPI, vol. 15(16), pages 1-17, August.
  19. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
  20. Chan-Uk Yeom & Keun-Chang Kwak, 2017. "Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation," Energies, MDPI, vol. 10(10), pages 1-18, October.
  21. Le Hoang Anh & Gwang-Hyun Yu & Dang Thanh Vu & Hyoung-Gook Kim & Jin-Young Kim, 2023. "DelayNet: Enhancing Temporal Feature Extraction for Electronic Consumption Forecasting with Delayed Dilated Convolution," Energies, MDPI, vol. 16(22), pages 1-18, November.
  22. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
  23. Luo, Shuman & Weng, Yang, 2019. "A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources," Applied Energy, Elsevier, vol. 242(C), pages 1497-1512.
  24. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
  25. Zhoufan Chen & Congmin Wang & Longjin Lv & Liangzhong Fan & Shiting Wen & Zhengtao Xiang, 2023. "Research on Peak Load Prediction of Distribution Network Lines Based on Prophet-LSTM Model," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
  26. Lin, Chenhao & Liang, Huijun & Pang, Aokang, 2023. "A fast data-driven optimization method of multi-area combined economic emission dispatch," Applied Energy, Elsevier, vol. 337(C).
  27. Ng, Rong Wang & Begam, Kasim Mumtaj & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2021. "An improved self-organizing incremental neural network model for short-term time-series load prediction," Applied Energy, Elsevier, vol. 292(C).
  28. Li, Yiyan & Zhang, Si & Hu, Rongxing & Lu, Ning, 2021. "A meta-learning based distribution system load forecasting model selection framework," Applied Energy, Elsevier, vol. 294(C).
  29. Khan, Noman & Khan, Samee Ullah & Baik, Sung Wook, 2023. "Deep dive into hybrid networks: A comparative study and novel architecture for efficient power prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  30. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
  31. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
  32. Ziras, Charalampos & Heinrich, Carsten & Bindner, Henrik W., 2021. "Why baselines are not suited for local flexibility markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
  33. Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
  34. Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
  35. Qiangqiang Cheng & Yiqi Yan & Shichao Liu & Chunsheng Yang & Hicham Chaoui & Mohamad Alzayed, 2020. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling," Energies, MDPI, vol. 13(24), pages 1-15, December.
  36. Kang, J. & Reiner, D., 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Cambridge Working Papers in Economics 2142, Faculty of Economics, University of Cambridge.
  37. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
  38. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
  39. Huang, Yanmei & Hasan, Najmul & Deng, Changrui & Bao, Yukun, 2022. "Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting," Energy, Elsevier, vol. 239(PC).
  40. Zhaorui Meng & Xianze Xu, 2019. "A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment," Energies, MDPI, vol. 12(24), pages 1-14, December.
  41. Lintao Yang & Honggeng Yang, 2019. "Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-23, April.
  42. Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).
  43. Yang, Qing & Zhang, Lei & Zou, Shaohui & Zhang, Jinsuo, 2020. "Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security," Energy Policy, Elsevier, vol. 139(C).
  44. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  45. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
  46. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
  47. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
  48. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
  49. Ottavia Valentini & Nikoleta Andreadou & Paolo Bertoldi & Alexandre Lucas & Iolanda Saviuc & Evangelos Kotsakis, 2022. "Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load," Energies, MDPI, vol. 15(14), pages 1-36, July.
  50. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
  51. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
  52. Majdi Frikha & Khaled Taouil & Ahmed Fakhfakh & Faouzi Derbel, 2024. "Predicting Power Consumption Using Deep Learning with Stationary Wavelet," Forecasting, MDPI, vol. 6(3), pages 1-21, September.
  53. Di Wang & Sha Li & Xiaojin Fu, 2024. "Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention," Energies, MDPI, vol. 17(16), pages 1-23, August.
  54. Liu, Ze & Xu, Sichuan & Zhao, Honghui & Wang, Yupeng, 2022. "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models," Applied Energy, Elsevier, vol. 326(C).
  55. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
  56. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
  57. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
  58. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
  59. Wu, Jinran & Cui, Zhesen & Chen, Yanyan & Kong, Demeng & Wang, You-Gan, 2019. "A new hybrid model to predict the electrical load in five states of Australia," Energy, Elsevier, vol. 166(C), pages 598-609.
  60. Mingping Liu & Yangze Li & Jiangong Hu & Xiaolong Wu & Suhui Deng & Hongqiao Li, 2023. "A New Hybrid Model Based on SCINet and LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 17(1), pages 1-20, December.
  61. Sergio Bruno & Gabriella Dellino & Massimo La Scala & Carlo Meloni, 2019. "A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †," Energies, MDPI, vol. 12(6), pages 1-20, March.
  62. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
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