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A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

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  1. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
  2. Morsali, Roozbeh & Thirunavukkarasu, Gokul Sidarth & Seyedmahmoudian, Mehdi & Stojcevski, Alex & Kowalczyk, Ryszard, 2020. "A relaxed constrained decentralised demand side management system of a community-based residential microgrid with realistic appliance models," Applied Energy, Elsevier, vol. 277(C).
  3. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
  4. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
  5. 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).
  6. Alderete Peralta, Ali & Balta-Ozkan, Nazmiye & Longhurst, Philip, 2022. "Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach," Applied Energy, Elsevier, vol. 305(C).
  7. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
  8. Marta P. Fernandes & Joaquim L. Viegas & Susana M. Vieira & João M. C. Sousa, 2017. "Segmentation of Residential Gas Consumers Using Clustering Analysis," Energies, MDPI, vol. 10(12), pages 1-26, December.
  9. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
  10. Brucke, Karoline & Arens, Stefan & Telle, Jan-Simon & Steens, Thomas & Hanke, Benedikt & von Maydell, Karsten & Agert, Carsten, 2021. "A non-intrusive load monitoring approach for very short-term power predictions in commercial buildings," Applied Energy, Elsevier, vol. 292(C).
  11. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
  12. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
  13. 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).
  14. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
  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. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  17. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
  18. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
  19. A. Bassam & O. May Tzuc & M. Escalante Soberanis & L. J. Ricalde & B. Cruz, 2017. "Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
  20. 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.
  21. Vogler–Finck, P.J.C. & Bacher, P. & Madsen, H., 2017. "Online short-term forecast of greenhouse heat load using a weather forecast service," Applied Energy, Elsevier, vol. 205(C), pages 1298-1310.
  22. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
  23. Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Shah, Ibrar Ali & Khan, Farrukh Aslam & Derhab, Abdelouahid, 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid," Applied Energy, Elsevier, vol. 299(C).
  24. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  25. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
  26. Nikos Kampelis & Elisavet Tsekeri & Dionysia Kolokotsa & Kostas Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2018. "Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions," Energies, MDPI, vol. 11(11), pages 1-22, November.
  27. Andrés Camero & Gabriel Luque & Yesnier Bravo & Enrique Alba, 2018. "Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case," Energies, MDPI, vol. 11(7), pages 1-14, July.
  28. Sudha, M. & Valarmathi, B., 2015. "Impact of Hybrid Intelligent Computing in Identifying Constructive Weather Parameters for Modeling Effective Rainfall Prediction," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 7(4), pages 1-10, December.
  29. Das, Laya & Munikoti, Sai & Natarajan, Balasubramaniam & Srinivasan, Babji, 2020. "Measuring smart grid resilience: Methods, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  30. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
  31. Severinsen, A. & Myrland, Ø., 2022. "Statistical learning to estimate energy savings from retrofitting in the Norwegian food retail market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  32. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
  33. Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2020. "Application of Artificial Neural Networks for Natural Gas Consumption Forecasting," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
  34. Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
  35. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
  36. Bedi, Jatin & Toshniwal, Durga, 2021. "Can electricity demand lead to air pollution? A spatio-temporal analysis of electricity demand with climatic conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
  37. 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.
  38. Moustris, K. & Kavadias, K.A. & Zafirakis, D. & Kaldellis, J.K., 2020. "Medium, short and very short-term prognosis of load demand for the Greek Island of Tilos using artificial neural networks and human thermal comfort-discomfort biometeorological data," Renewable Energy, Elsevier, vol. 147(P1), pages 100-109.
  39. Guowei Cai & Wenjin Wang & Junhai Lu, 2016. "A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction," Energies, MDPI, vol. 9(12), pages 1-19, November.
  40. Mahmoud Elkazaz & Mark Sumner & David Thomas, 2019. "Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification," Energies, MDPI, vol. 12(14), pages 1-26, July.
  41. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
  42. 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).
  43. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
  44. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Reis, Agnaldo J.R. & Enayatifar, Rasul & Souza, Marcone J.F. & Guimarães, Frederico G., 2016. "A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment," Applied Energy, Elsevier, vol. 169(C), pages 567-584.
  45. 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.
  46. Lv, Chaoxian & Yu, Hao & Li, Peng & Wang, Chengshan & Xu, Xiandong & Li, Shuquan & Wu, Jianzhong, 2019. "Model predictive control based robust scheduling of community integrated energy system with operational flexibility," Applied Energy, Elsevier, vol. 243(C), pages 250-265.
  47. Hongjun Guan & Zongli Dai & Aiwu Zhao & Jie He, 2018. "A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-15, February.
  48. Dohnal, Mirko, 2016. "Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 676-684.
  49. F. Marta L. Di Lascio & Andrea Menapace & Maurizio Righetti, 2020. "Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 373-395, June.
  50. Fabio Corti & Antonino Laudani & Gabriele Maria Lozito & Alberto Reatti, 2020. "Computationally Efficient Modeling of DC-DC Converters for PV Applications," Energies, MDPI, vol. 13(19), pages 1-18, September.
  51. 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.
  52. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
  53. Mason, Karl & Duggan, Jim & Howley, Enda, 2018. "Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks," Energy, Elsevier, vol. 155(C), pages 705-720.
  54. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
  55. Soares, N. & Martins, A.G. & Carvalho, A.L. & Caldeira, C. & Du, C. & Castanheira, É. & Rodrigues, E. & Oliveira, G. & Pereira, G.I. & Bastos, J. & Ferreira, J.P. & Ribeiro, L.A. & Figueiredo, N.C. & , 2018. "The challenging paradigm of interrelated energy systems towards a more sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 171-193.
  56. Canan G. Corlu & Rocio de la Torre & Adrian Serrano-Hernandez & Angel A. Juan & Javier Faulin, 2020. "Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities," Energies, MDPI, vol. 13(5), pages 1-33, March.
  57. Lin, Qiliang & Chen, Yi-Chung & Chen, Fangliang & DeGanyar, Tejav & Yin, Huiming, 2022. "Design and experiments of a thermoelectric-powered wireless sensor network platform for smart building envelope," Applied Energy, Elsevier, vol. 305(C).
  58. Ashfaq Ahmad & Nadeem Javaid & Abdul Mateen & Muhammad Awais & Zahoor Ali Khan, 2019. "Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach," Energies, MDPI, vol. 12(1), pages 1-21, January.
  59. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
  60. Juncheng Zhu & Zhile Yang & Monjur Mourshed & Yuanjun Guo & Yimin Zhou & Yan Chang & Yanjie Wei & Shengzhong Feng, 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches," Energies, MDPI, vol. 12(14), pages 1-19, July.
  61. Bakar, Nur Najihah Abu & Bazmohammadi, Najmeh & Çimen, Halil & Uyanik, Tayfun & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Data-driven ship berthing forecasting for cold ironing in maritime transportation," Applied Energy, Elsevier, vol. 326(C).
  62. Singh, Priyanka & Dwivedi, Pragya & Kant, Vibhor, 2019. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting," Energy, Elsevier, vol. 174(C), pages 460-477.
  63. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
  64. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
  65. Manuel R. Arahal & Manuel G. Ortega & Manuel G. Satué, 2021. "Chiller Load Forecasting Using Hyper-Gaussian Nets," Energies, MDPI, vol. 14(12), pages 1-15, June.
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  67. Andrés M. Alonso & Francisco J. Nogales & Carlos Ruiz, 2020. "A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series," Energies, MDPI, vol. 13(20), pages 1-19, October.
  68. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.
  69. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
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  71. Pengwei Su & Xue Tian & Yan Wang & Shuai Deng & Jun Zhao & Qingsong An & Yongzhen Wang, 2017. "Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System," Energies, MDPI, vol. 10(9), pages 1-13, August.
  72. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
  73. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
  74. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  75. Verstraete, Gylian & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "A data-driven framework for predicting weather impact on high-volume low-margin retail products," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 169-177.
  76. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
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  79. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
  80. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
  81. Kofi Afrifa Agyeman & Gyeonggak Kim & Hoonyeon Jo & Seunghyeon Park & Sekyung Han, 2020. "An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads," Energies, MDPI, vol. 13(10), pages 1-20, May.
  82. Geun-Cheol Lee, 2022. "Regression-Based Methods for Daily Peak Load Forecasting in South Korea," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
  83. Maria C. Fotopoulou & Panagiotis Drosatos & Stefanos Petridis & Dimitrios Rakopoulos & Fotis Stergiopoulos & Nikolaos Nikolopoulos, 2021. "Model Predictive Control for the Energy Management in a District of Buildings Equipped with Building Integrated Photovoltaic Systems and Batteries," Energies, MDPI, vol. 14(12), pages 1-21, June.
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  86. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Predicting plug loads with occupant count data through a deep learning approach," Energy, Elsevier, vol. 181(C), pages 29-42.
  87. Zhang, Wanqing & Lin, Zi & Liu, Xiaolei, 2022. "Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Te," Renewable Energy, Elsevier, vol. 185(C), pages 611-628.
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  93. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
  94. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
  95. Ulaa AlHaddad & Abdullah Basuhail & Maher Khemakhem & Fathy Elbouraey Eassa & Kamal Jambi, 2023. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
  96. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
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  99. 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.
  100. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
  101. Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1-24, January.
  102. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
  103. Kasım Zor & Özgür Çelik & Oğuzhan Timur & Ahmet Teke, 2020. "Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks," Energies, MDPI, vol. 13(5), pages 1-24, March.
  104. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
  105. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
  106. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
  107. Balmer, Roberto E. & Levin, Stanford L. & Schmidt, Stephen, 2020. "Artificial Intelligence Applications in Telecommunications and other network industries," Telecommunications Policy, Elsevier, vol. 44(6).
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