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Short-term residential load forecasting: Impact of calendar effects and forecast granularity

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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. Fang Xu & Meng Tian & Jie Yang & Guohu Xu, 2020. "Does Environmental Inspection Led by the Central Government Improve the Air Quality in China? The Moderating Role of Public Engagement," Sustainability, MDPI, vol. 12(8), pages 1-27, April.
  3. Fermín Rodríguez & Fernando Martín & Luis Fontán & Ainhoa Galarza, 2020. "Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control," Energies, MDPI, vol. 13(19), pages 1-19, October.
  4. 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.
  5. Luca Massidda & Marino Marrocu, 2018. "Smart Meter Forecasting from One Minute to One Year Horizons," Energies, MDPI, vol. 11(12), pages 1-16, December.
  6. Shepero, Mahmoud & van der Meer, Dennis & Munkhammar, Joakim & Widén, Joakim, 2018. "Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data," Applied Energy, Elsevier, vol. 218(C), pages 159-172.
  7. Filipe Rodrigues & Carlos Cardeira & João M. F. Calado & Rui Melicio, 2023. "Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review," Energies, MDPI, vol. 16(10), pages 1-26, May.
  8. Burleyson, Casey D. & Rahman, Aowabin & Rice, Jennie S. & Smith, Amanda D. & Voisin, Nathalie, 2021. "Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States," Applied Energy, Elsevier, vol. 304(C).
  9. Gang Chen & Qingchang Hu & Jin Wang & Xu Wang & Yuyu Zhu, 2023. "Machine-Learning-Based Electric Power Forecasting," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
  10. Wenhao Chen & Guangjie Han & Hongbo Zhu & Lyuchao Liao, 2022. "Short-Term Load Forecasting with an Ensemble Model Using Densely Residual Block and Bi-LSTM Based on the Attention Mechanism," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
  11. Pan, Keda & Chen, Zhaohua & Lai, Chun Sing & Xie, Changhong & Wang, Dongxiao & Li, Xuecong & Zhao, Zhuoli & Tong, Ning & Lai, Loi Lei, 2022. "An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation," Applied Energy, Elsevier, vol. 309(C).
  12. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
  13. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
  14. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
  15. Khoshrou, Abdolrahman & Pauwels, Eric J., 2019. "Short-term scenario-based probabilistic load forecasting: A data-driven approach," Applied Energy, Elsevier, vol. 238(C), pages 1258-1268.
  16. Fan, Cheng & Wang, Jiayuan & Gang, Wenjie & Li, Shenghan, 2019. "Assessment of deep recurrent neural network-based strategies for short-term building energy predictions," Applied Energy, Elsevier, vol. 236(C), pages 700-710.
  17. Sekhar, Charan & Dahiya, Ratna, 2023. "Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand," Energy, Elsevier, vol. 268(C).
  18. Ping Ma & Shuhui Cui & Mingshuai Chen & Shengzhe Zhou & Kai Wang, 2023. "Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System," Energies, MDPI, vol. 16(15), pages 1-17, August.
  19. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
  20. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
  21. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
  22. Hasan Erteza Gelani & Faizan Dastgeer & Mashood Nasir & Sidra Khan & Josep M. Guerrero, 2021. "AC vs. DC Distribution Efficiency: Are We on the Right Path?," Energies, MDPI, vol. 14(13), pages 1-26, July.
  23. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
  24. Gde Dharma Nugraha & Ardiansyah Musa & Jaiyoung Cho & Kishik Park & Deokjai Choi, 2018. "Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings," Energies, MDPI, vol. 11(4), pages 1-20, March.
  25. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
  26. Mehdi Seyedmahmoudian & Elmira Jamei & Gokul Sidarth Thirunavukkarasu & Tey Kok Soon & Michael Mortimer & Ben Horan & Alex Stojcevski & Saad Mekhilef, 2018. "Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach," Energies, MDPI, vol. 11(5), pages 1-23, May.
  27. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
  28. Zheng, Zhuang & Sun, Zhankun & Pan, Jia & Luo, Xiaowei, 2021. "An integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems," Applied Energy, Elsevier, vol. 298(C).
  29. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
  30. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
  31. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
  32. Ahajjam, Mohamed Aymane & Bonilla Licea, Daniel & Ghogho, Mounir & Kobbane, Abdellatif, 2022. "Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting," Applied Energy, Elsevier, vol. 326(C).
  33. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
  34. Michael Wood & Emanuele Ogliari & Alfredo Nespoli & Travis Simpkins & Sonia Leva, 2023. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies," Forecasting, MDPI, vol. 5(1), pages 1-18, March.
  35. Wenhui Zhao & Tong Li & Danyang Xu & Zhaohua Wang, 2024. "A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model," Annals of Operations Research, Springer, vol. 339(1), pages 227-259, August.
  36. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
  37. Alipour, Panteha & Mukherjee, Sayanti & Nateghi, Roshanak, 2019. "Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region," Energy, Elsevier, vol. 185(C), pages 1143-1153.
  38. Iuri C. Figueiró & Alzenira R. Abaide & Nelson K. Neto & Leonardo N. F. Silva & Laura L. C. Santos, 2023. "Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region," Energies, MDPI, vol. 16(19), pages 1-21, September.
  39. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
  40. Dadkhah, Mojtaba & Jahangoshai Rezaee, Mustafa & Zare Chavoshi, Ahmad, 2018. "Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed," Energy, Elsevier, vol. 148(C), pages 775-788.
  41. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.
  42. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
  43. Czarina Copiaco & Mutasim Nour, 2024. "Optimizing the Operation of Grid-Interactive Efficient Buildings (GEBs) Using Machine Learning," Sustainability, MDPI, vol. 16(20), pages 1-18, October.
  44. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
  45. Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
  46. Ali K k & Erg n Y kseltan & Mustafa Hekimo lu & Esra Agca Aktunc & Ahmet Y cekaya & Ay e Bilge, 2022. "Forecasting Hourly Electricity Demand Under COVID-19 Restrictions," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 12(1), pages 73-85.
  47. Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
  48. 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).
  49. Lee, Eunjung & Lee, Kyungeun & Lee, Hyoseop & Kim, Euncheol & Rhee, Wonjong, 2019. "Defining virtual control group to improve customer baseline load calculation of residential demand response," Applied Energy, Elsevier, vol. 250(C), pages 946-958.
  50. Park, June Young & Yang, Xiya & Miller, Clayton & Arjunan, Pandarasamy & Nagy, Zoltan, 2019. "Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset," Applied Energy, Elsevier, vol. 236(C), pages 1280-1295.
  51. Hussain, I. & Ali, S.M. & Khan, B. & Ullah, Z. & Mehmood, C.A. & Jawad, M. & Farid, U. & Haider, A., 2019. "Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers," Renewable Energy, Elsevier, vol. 134(C), pages 1017-1033.
  52. Li, Kun & Cursio, Joseph D. & Jiang, Mengfei & Liang, Xi, 2019. "The significance of calendar effects in the electricity market," Applied Energy, Elsevier, vol. 235(C), pages 487-494.
  53. Haoda Ye & Qiuyu Zhu & Xuefan Zhang, 2024. "Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer," Energies, MDPI, vol. 17(13), pages 1-22, June.
  54. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
  55. Guefano, Serge & Tamba, Jean Gaston & Azong, Tchitile Emmanuel Wilfried & Monkam, Louis, 2021. "Forecast of electricity consumption in the Cameroonian residential sector by Grey and vector autoregressive models," Energy, Elsevier, vol. 214(C).
  56. Tulin Guzel & Hakan Cinar & Mehmet Nabi Cenet & Kamil Doruk Oguz & Ahmet Yucekaya & Mustafa Hekimoglu, 2023. "A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 13(5), pages 179-193, September.
  57. Khan, Waqas & Liao, Juo Yu & Walker, Shalika & Zeiler, Wim, 2022. "Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism," Applied Energy, Elsevier, vol. 319(C).
  58. Hyojoo Son & Changwan Kim, 2020. "A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
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