A combined deep learning application for short term load forecasting
An accurate prediction of buildings' load demand is one of the most important issues in smart grid and smart building applications. In this way, an important contribution is made to improving the reliability of the power system, facilitating the integration of renewable energy sources and makin...
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| Vydané v: | Alexandria engineering journal Ročník 60; číslo 4; s. 3807 - 3818 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.08.2021
Elsevier |
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| ISSN: | 1110-0168 |
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| Abstract | An accurate prediction of buildings' load demand is one of the most important issues in smart grid and smart building applications. In this way, an important contribution is made to improving the reliability of the power system, facilitating the integration of renewable energy sources and making demand response processes more effective. Nowadays, the electricity prediction based on sensor data has become quite common with the increasing popularity of smart meter applications. While such approaches produce very successful results, they often require large amounts of data recorded over long periods of time during the training of machine learning models to make accurate predictions. The fact that smart meter and sensor applications are becoming more widespread in different parts of the world and that the newly constructed buildings and new meters have relatively small historical data is an important constraint for sensor-based approaches. In this article, a cross-correlation based transfer learning approach is proposed on getting data from different parts of the world to obtain more successful predictive results with limited data. Results of application on actual energy system validate the advantages of proposed model. |
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| AbstractList | An accurate prediction of buildings' load demand is one of the most important issues in smart grid and smart building applications. In this way, an important contribution is made to improving the reliability of the power system, facilitating the integration of renewable energy sources and making demand response processes more effective. Nowadays, the electricity prediction based on sensor data has become quite common with the increasing popularity of smart meter applications. While such approaches produce very successful results, they often require large amounts of data recorded over long periods of time during the training of machine learning models to make accurate predictions. The fact that smart meter and sensor applications are becoming more widespread in different parts of the world and that the newly constructed buildings and new meters have relatively small historical data is an important constraint for sensor-based approaches. In this article, a cross-correlation based transfer learning approach is proposed on getting data from different parts of the world to obtain more successful predictive results with limited data. Results of application on actual energy system validate the advantages of proposed model. |
| Author | Efe, Serhat Berat Ozer, Ilyas Ozbay, Harun |
| Author_xml | – sequence: 1 givenname: Ilyas orcidid: 0000-0003-2112-5497 surname: Ozer fullname: Ozer, Ilyas email: iozer@bandirma.edu.tr organization: Department of Computer Engineering, Bandirma Onyedi Eylul University, 10200 Balikesir, Turkey – sequence: 2 givenname: Serhat Berat surname: Efe fullname: Efe, Serhat Berat email: sefe@bandirma.edu.tr organization: Department of Electrical Engineering, Bandirma Onyedi Eylul University, 10200 Balikesir, Turkey – sequence: 3 givenname: Harun surname: Ozbay fullname: Ozbay, Harun email: hozbay@bandirma.edu.tr organization: Department of Electrical Engineering, Bandirma Onyedi Eylul University, 10200 Balikesir, Turkey |
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| Cites_doi | 10.1109/TSG.2018.2818167 10.1016/j.segan.2017.03.001 10.1016/j.ijepes.2018.11.022 10.1016/j.segan.2020.100308 10.1016/j.energy.2019.03.081 10.1109/TII.2015.2414355 10.1016/j.asoc.2019.105616 10.1109/ACCESS.2017.2696365 10.1109/TEVC.2005.857075 10.1016/j.aej.2020.06.008 10.1016/j.enbuild.2016.01.030 10.1016/j.enbuild.2014.02.011 10.1016/j.energy.2020.117087 10.1016/j.egyr.2019.08.086 10.1016/j.enbuild.2018.01.034 10.1016/j.knosys.2018.05.021 10.1016/j.aej.2020.06.049 10.1109/TII.2017.2711648 10.1109/ACCESS.2019.2943752 10.1049/iet-rpg.2016.1036 10.1016/j.enbuild.2020.109941 10.1016/j.knosys.2018.08.027 10.1016/j.apenergy.2017.03.064 10.1016/j.egypro.2019.01.952 10.1109/TKDE.2009.191 10.1162/neco.1997.9.8.1735 10.1016/j.energy.2020.118874 10.1049/iet-smt.2013.0135 10.1007/s10115-020-01481-0 10.1016/j.energy.2020.117127 10.1016/j.enbuild.2019.04.034 10.1016/j.enconman.2015.07.041 10.1016/j.eij.2018.11.001 10.1109/ACCESS.2019.2901920 10.1016/j.procs.2017.11.374 10.1016/j.neucom.2020.01.031 10.1161/JAHA.118.008678 |
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| Keywords | Load forecasting Transfer learning Cross-correlation Electrical power systems |
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| References | Sadaei, de Lima e Silva, Guimarães, Lee (b0105) 2019; 175 L'Heureux, Grolinger, Elyamany, Capretz (b0185) 2017; 5 Ju, Sun, Chen, Zhang, Zhu, Rehman (b0230) 2019; 7 Le, Vo, Kieu, Hwang, Rho, Sung (b0130) 2020; 20 Moon, Kim, Kang, Hwang (b0125) 2020; 13 Ribeiro, Grolinger, ElYamany, Higashino, Capretz (b0150) 2018; 165 Tian, Sehovac, Grolinger (b0040) 2019; 7 Ye, Dai (b0135) 2018; 156 Fan, Xiao, Zhao (b0210) 2017; 195 Alahakoon, Yu (b0015) 2016; 12 Lu, Zhang, Chen, Seng (b0160) 2021; 60 Kim, Son, Kim (b0145) 2019; 5 Fan, Ding, Zheng, Xiao, Ai (b0085) 2020; 388 Liu, Li, Li, Yan, Saha (b0095) 2017; 11 Grolinger, Hayes, Higashino, L’Heureux, Allison, Capretz (b0180) 2014 Lahouar, Ben Hadj Slama (b0220) 2015; 103 Yang, Li, Yang (b0170) 2019; 163 Bracale, Carpinelli, De Falco, Hong (b0025) 2019; 107 Malekizadeh, Karami, Karimi, Moshari, Sanjari (b0080) 2020; 196 Access Date: 10.04.2020. Mocanu, Nguyen, Kling, Gibescu (b0140) 2016; 116 Maldonado, González, Crone (b0060) 2019; 83 Larsen, Pinson, Leimgruber, Judex (b0050) 2017; 10 Muzaffar, Afshari (b0070) 2019; 158 He (b0100) 2017; 122 De Giorgi, Congedo, Malvoni (b0090) 2014; 8 Wen, Zhou, Yang (b0195) 2020; 179 D.P. Kingma, J.L. Ba, Adam: a method for stochastic optimization, in: 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings, 2015, pp. 1–15. Massaoudi, Refaat, Chihi, Trabelsi, Oueslati, Abu-Rub (b0225) 2021; 214 Wang, Chen, Hong, Kang (b0035) 2019; 10 Liao, Tsao (b0075) 2006; 10 Beretta, Grillo, Pigoli, Bionda, Bossi, Tornelli (b0055) 2020; 21 Bashar, Nayak, Suzor (b0120) 2020; 62 Liu, Zhang, Song (b0045) 2020; 119 Kim, Moon, Hwang, Kang (b0110) 2019; 194 Talaat, Farahat, Mansour, Hatata (b0005) 2020; 196 Xia, Pan, Yan, Cai, Yan, Ning (b0235) 2020; 37 Huang, Hong, Li (b0020) 2017; 13 Kwon, Lee, Lee, Lee, Park (b0175) 2018; 7 Özer (b0190) 2019 Aly (b0010) 2020; 182 Ma, Cheng, Jiang, Chen, Wang, Zhai (b0115) 2020; 216 Hochreiter, Schmidhuber (b0155) 1997; 9 Pan, Yang (b0205) 2010; 22 Menezes, Cripps, Buswell, Wright, Bouchlaghem (b0030) 2014; 75 Wu, Cattani, Song, Zio (b0065) 2020; 59 Walia, Josan, Singh (b0165) 2019; 20 Huang (10.1016/j.aej.2021.02.050_b0020) 2017; 13 Fan (10.1016/j.aej.2021.02.050_b0210) 2017; 195 10.1016/j.aej.2021.02.050_b0215 Grolinger (10.1016/j.aej.2021.02.050_b0180) 2014 Kim (10.1016/j.aej.2021.02.050_b0145) 2019; 5 Kwon (10.1016/j.aej.2021.02.050_b0175) 2018; 7 Muzaffar (10.1016/j.aej.2021.02.050_b0070) 2019; 158 Ma (10.1016/j.aej.2021.02.050_b0115) 2020; 216 He (10.1016/j.aej.2021.02.050_b0100) 2017; 122 Bashar (10.1016/j.aej.2021.02.050_b0120) 2020; 62 Hochreiter (10.1016/j.aej.2021.02.050_b0155) 1997; 9 Özer (10.1016/j.aej.2021.02.050_b0190) 2019 Le (10.1016/j.aej.2021.02.050_b0130) 2020; 20 Lu (10.1016/j.aej.2021.02.050_b0160) 2021; 60 Moon (10.1016/j.aej.2021.02.050_b0125) 2020; 13 Ribeiro (10.1016/j.aej.2021.02.050_b0150) 2018; 165 Tian (10.1016/j.aej.2021.02.050_b0040) 2019; 7 Wen (10.1016/j.aej.2021.02.050_b0195) 2020; 179 Malekizadeh (10.1016/j.aej.2021.02.050_b0080) 2020; 196 Beretta (10.1016/j.aej.2021.02.050_b0055) 2020; 21 Ye (10.1016/j.aej.2021.02.050_b0135) 2018; 156 Pan (10.1016/j.aej.2021.02.050_b0205) 2010; 22 Talaat (10.1016/j.aej.2021.02.050_b0005) 2020; 196 Menezes (10.1016/j.aej.2021.02.050_b0030) 2014; 75 Kim (10.1016/j.aej.2021.02.050_b0110) 2019; 194 Ju (10.1016/j.aej.2021.02.050_b0230) 2019; 7 Liu (10.1016/j.aej.2021.02.050_b0045) 2020; 119 Mocanu (10.1016/j.aej.2021.02.050_b0140) 2016; 116 Lahouar (10.1016/j.aej.2021.02.050_b0220) 2015; 103 Alahakoon (10.1016/j.aej.2021.02.050_b0015) 2016; 12 Wu (10.1016/j.aej.2021.02.050_b0065) 2020; 59 Larsen (10.1016/j.aej.2021.02.050_b0050) 2017; 10 Bracale (10.1016/j.aej.2021.02.050_b0025) 2019; 107 Fan (10.1016/j.aej.2021.02.050_b0085) 2020; 388 Wang (10.1016/j.aej.2021.02.050_b0035) 2019; 10 Massaoudi (10.1016/j.aej.2021.02.050_b0225) 2021; 214 Yang (10.1016/j.aej.2021.02.050_b0170) 2019; 163 L'Heureux (10.1016/j.aej.2021.02.050_b0185) 2017; 5 Liu (10.1016/j.aej.2021.02.050_b0095) 2017; 11 Sadaei (10.1016/j.aej.2021.02.050_b0105) 2019; 175 Maldonado (10.1016/j.aej.2021.02.050_b0060) 2019; 83 Xia (10.1016/j.aej.2021.02.050_b0235) 2020; 37 Walia (10.1016/j.aej.2021.02.050_b0165) 2019; 20 Liao (10.1016/j.aej.2021.02.050_b0075) 2006; 10 De Giorgi (10.1016/j.aej.2021.02.050_b0090) 2014; 8 10.1016/j.aej.2021.02.050_b0200 Aly (10.1016/j.aej.2021.02.050_b0010) 2020; 182 |
| References_xml | – volume: 103 start-page: 1040 year: 2015 end-page: 1051 ident: b0220 article-title: Day-ahead load forecast using random forest and expert input selection publication-title: Energy Convers. Manage. – volume: 60 start-page: 87 year: 2021 end-page: 94 ident: b0160 article-title: A combined method for short-term traffic flow prediction based on recurrent neural network publication-title: Alexandria Eng. J. – volume: 7 start-page: 139895 year: 2019 end-page: 139908 ident: b0040 article-title: Similarity-based chained transfer learning for energy forecasting with big data publication-title: IEEE Access – volume: 182 year: 2020 ident: b0010 article-title: A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid publication-title: Electr. Power Syst. Res. – volume: 107 start-page: 177 year: 2019 end-page: 185 ident: b0025 article-title: Short-term industrial reactive power forecasting publication-title: Int. J. Electric. Power Energy Syst. – volume: 8 start-page: 90 year: 2014 end-page: 97 ident: b0090 article-title: Photovoltaic power forecasting using statistical methods: impact of weather data publication-title: IET Sci. Measur. Technol. – volume: 196 start-page: 117087 year: 2020 ident: b0005 article-title: Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach publication-title: Energy – volume: 122 start-page: 308 year: 2017 end-page: 314 ident: b0100 article-title: load forecasting via deep neural networks publication-title: Procedia Comput. Sci. – year: 2019 ident: b0190 article-title: The Effect of Normalization on the Classification of Traffic Comments – volume: 59 start-page: 3111 year: 2020 end-page: 3118 ident: b0065 article-title: Fractional ARIMA with an improved cuckoo search optimization for the efficient Short-term power load forecasting publication-title: Alexandria Eng. J. – start-page: 182 year: 2014 end-page: 189 ident: b0180 article-title: Challenges for MapReduce in big data publication-title: IEEE 10th World Congress on Services – volume: 11 start-page: 1281 year: 2017 end-page: 1287 ident: b0095 article-title: Takagi–Sugeno fuzzy model‐based approach considering multiple weather factors for the photovoltaic power short‐term forecasting publication-title: IET Renew. Power Gen. – volume: 13 start-page: 1 year: 2020 end-page: 37 ident: b0125 article-title: Solving the cold-start problem in short-term load publication-title: Energies – volume: 179 year: 2020 ident: b0195 article-title: Load demand forecasting of residential buildings using a deep learning model publication-title: Electr. Power Syst. Res. – reference: D.P. Kingma, J.L. Ba, Adam: a method for stochastic optimization, in: 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings, 2015, pp. 1–15. – volume: 216 start-page: 1 year: 2020 end-page: 9 ident: b0115 article-title: A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data publication-title: Energy Build. – volume: 7 start-page: 1 year: 2018 end-page: 11 ident: b0175 article-title: An algorithm based on deep learning for predicting in-hospital cardiac arrest publication-title: J. Am. Heart Assoc. – volume: 75 start-page: 199 year: 2014 end-page: 209 ident: b0030 article-title: Estimating the energy consumption and power demand of small power equipment in office buildings publication-title: Energy Build. – volume: 175 start-page: 365 year: 2019 end-page: 377 ident: b0105 article-title: Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series publication-title: Energy – volume: 156 start-page: 74 year: 2018 end-page: 99 ident: b0135 article-title: A novel transfer learning framework for time series forecasting publication-title: Knowl. Syst. – volume: 20 start-page: 89 year: 2019 end-page: 96 ident: b0165 article-title: An efficient automated answer scoring system for Punjabi language publication-title: Egyptian Inform. J. – volume: 119 year: 2020 ident: b0045 article-title: A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: from classical data mining to deep learning publication-title: Renew. Sustain. Energy Rev. – volume: 13 start-page: 2886 year: 2017 end-page: 2898 ident: b0020 article-title: Hour-ahead price based energy management scheme for industrial facilities publication-title: IEEE Trans. Ind. Inf. – volume: 62 start-page: 4029 year: 2020 end-page: 4054 ident: b0120 article-title: Regularising LSTM classifier by transfer learning for detecting misogynistic tweets with small training set publication-title: Knowl. Inf. Syst. – volume: 195 start-page: 222 year: 2017 end-page: 233 ident: b0210 article-title: A short-term building cooling load prediction method using deep learning algorithms publication-title: Appl. Energy – volume: 83 start-page: 105616 year: 2019 ident: b0060 article-title: Automatic time series analysis for electric load forecasting via support vector regression publication-title: Appl. Soft Comput. – volume: 388 start-page: 110 year: 2020 end-page: 123 ident: b0085 article-title: Empirical mode decomposition based multi-objective deep belief network for short-term power load forecasting publication-title: Neurocomputing – volume: 163 start-page: 159 year: 2019 end-page: 173 ident: b0170 article-title: Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines publication-title: Knowl. Syst. – volume: 7 start-page: 28309 year: 2019 end-page: 28318 ident: b0230 article-title: A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting publication-title: IEEE Access – volume: 21 start-page: 100308 year: 2020 ident: b0055 article-title: Functional principal component analysis as a versatile technique to understand and predict the electric consumption patterns publication-title: Sustain. Energy Grids Netw. – volume: 12 start-page: 425 year: 2016 end-page: 436 ident: b0015 article-title: Smart electricity meter data intelligence for future energy systems: a survey publication-title: IEEE Trans. Ind. Inf. – volume: 20 start-page: 1 year: 2020 end-page: 17 ident: b0130 article-title: Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building publication-title: Sensors – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: b0155 article-title: Long short-term memory publication-title: Neural Comput. – volume: 5 start-page: 7776 year: 2017 end-page: 7797 ident: b0185 article-title: Machine Learning with big data: challenges and approaches publication-title: IEEE Access – volume: 10 start-page: 3125 year: 2019 end-page: 3148 ident: b0035 article-title: Review of smart meter data analytics: applications, methodologies, and challenges publication-title: IEEE Trans. Smart Grid – volume: 10 start-page: 75 year: 2017 end-page: 83 ident: b0050 article-title: Demand response evaluation and forecasting—methods and results from the EcoGrid EU experiment publication-title: Sustain. Energy Grids Netw. – volume: 165 start-page: 352 year: 2018 end-page: 363 ident: b0150 article-title: Transfer learning with seasonal and trend adjustment for cross-building energy forecasting publication-title: Energy Build. – volume: 214 start-page: 118874 year: 2021 ident: b0225 article-title: A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting publication-title: Energy – volume: 116 start-page: 646 year: 2016 end-page: 655 ident: b0140 article-title: Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning publication-title: Energy Build. – volume: 10 start-page: 330 year: 2006 end-page: 340 ident: b0075 article-title: Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting publication-title: IEEE Trans. Evol. Comput. – volume: 196 start-page: 117127 year: 2020 ident: b0080 article-title: Short-term load forecast using ensemble neuro-fuzzy model publication-title: Energy – volume: 5 start-page: 1270 year: 2019 end-page: 1280 ident: b0145 article-title: Short term electricity load forecasting for institutional buildings publication-title: Energy Reports – volume: 158 start-page: 2922 year: 2019 end-page: 2927 ident: b0070 article-title: Short-term load forecasts using LSTM networks publication-title: Energy Procedia – volume: 37 start-page: 1 year: 2020 end-page: 9 ident: b0235 article-title: Prognostic model of small sample critical diseases based on transfer learning publication-title: J. Biomed. Eng. – volume: 194 start-page: 328 year: 2019 end-page: 341 ident: b0110 article-title: Recurrent inception convolution neural network for multi short-term load forecasting publication-title: Energy Build. – reference: . Access Date: 10.04.2020. – volume: 22 start-page: 1345 year: 2010 end-page: 1359 ident: b0205 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. – volume: 10 start-page: 3125 issue: 3 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0035 article-title: Review of smart meter data analytics: applications, methodologies, and challenges publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2018.2818167 – volume: 10 start-page: 75 year: 2017 ident: 10.1016/j.aej.2021.02.050_b0050 article-title: Demand response evaluation and forecasting—methods and results from the EcoGrid EU experiment publication-title: Sustain. Energy Grids Netw. doi: 10.1016/j.segan.2017.03.001 – volume: 107 start-page: 177 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0025 article-title: Short-term industrial reactive power forecasting publication-title: Int. J. Electric. Power Energy Syst. doi: 10.1016/j.ijepes.2018.11.022 – volume: 21 start-page: 100308 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0055 article-title: Functional principal component analysis as a versatile technique to understand and predict the electric consumption patterns publication-title: Sustain. Energy Grids Netw. doi: 10.1016/j.segan.2020.100308 – volume: 175 start-page: 365 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0105 article-title: Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series publication-title: Energy doi: 10.1016/j.energy.2019.03.081 – year: 2019 ident: 10.1016/j.aej.2021.02.050_b0190 – volume: 37 start-page: 1 issue: 1 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0235 article-title: Prognostic model of small sample critical diseases based on transfer learning publication-title: J. Biomed. Eng. – volume: 12 start-page: 425 issue: 1 year: 2016 ident: 10.1016/j.aej.2021.02.050_b0015 article-title: Smart electricity meter data intelligence for future energy systems: a survey publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2015.2414355 – volume: 83 start-page: 105616 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0060 article-title: Automatic time series analysis for electric load forecasting via support vector regression publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105616 – volume: 5 start-page: 7776 year: 2017 ident: 10.1016/j.aej.2021.02.050_b0185 article-title: Machine Learning with big data: challenges and approaches publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2696365 – volume: 179 issue: October 2019 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0195 article-title: Load demand forecasting of residential buildings using a deep learning model publication-title: Electr. Power Syst. Res. – volume: 10 start-page: 330 issue: 3 year: 2006 ident: 10.1016/j.aej.2021.02.050_b0075 article-title: Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2005.857075 – start-page: 182 year: 2014 ident: 10.1016/j.aej.2021.02.050_b0180 article-title: Challenges for MapReduce in big data – volume: 60 start-page: 87 issue: 1 year: 2021 ident: 10.1016/j.aej.2021.02.050_b0160 article-title: A combined method for short-term traffic flow prediction based on recurrent neural network publication-title: Alexandria Eng. J. doi: 10.1016/j.aej.2020.06.008 – volume: 119 issue: April 2019 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0045 article-title: A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: from classical data mining to deep learning publication-title: Renew. Sustain. Energy Rev. – volume: 116 start-page: 646 year: 2016 ident: 10.1016/j.aej.2021.02.050_b0140 article-title: Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning publication-title: Energy Build. doi: 10.1016/j.enbuild.2016.01.030 – volume: 75 start-page: 199 year: 2014 ident: 10.1016/j.aej.2021.02.050_b0030 article-title: Estimating the energy consumption and power demand of small power equipment in office buildings publication-title: Energy Build. doi: 10.1016/j.enbuild.2014.02.011 – volume: 196 start-page: 117087 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0005 article-title: Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach publication-title: Energy doi: 10.1016/j.energy.2020.117087 – volume: 5 start-page: 1270 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0145 article-title: Short term electricity load forecasting for institutional buildings publication-title: Energy Reports doi: 10.1016/j.egyr.2019.08.086 – volume: 165 start-page: 352 year: 2018 ident: 10.1016/j.aej.2021.02.050_b0150 article-title: Transfer learning with seasonal and trend adjustment for cross-building energy forecasting publication-title: Energy Build. doi: 10.1016/j.enbuild.2018.01.034 – volume: 156 start-page: 74 year: 2018 ident: 10.1016/j.aej.2021.02.050_b0135 article-title: A novel transfer learning framework for time series forecasting publication-title: Knowl. Syst. doi: 10.1016/j.knosys.2018.05.021 – volume: 59 start-page: 3111 issue: 5 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0065 article-title: Fractional ARIMA with an improved cuckoo search optimization for the efficient Short-term power load forecasting publication-title: Alexandria Eng. J. doi: 10.1016/j.aej.2020.06.049 – volume: 13 start-page: 2886 issue: 6 year: 2017 ident: 10.1016/j.aej.2021.02.050_b0020 article-title: Hour-ahead price based energy management scheme for industrial facilities publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2017.2711648 – volume: 7 start-page: 139895 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0040 article-title: Similarity-based chained transfer learning for energy forecasting with big data publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2943752 – volume: 11 start-page: 1281 issue: 10 year: 2017 ident: 10.1016/j.aej.2021.02.050_b0095 article-title: Takagi–Sugeno fuzzy model‐based approach considering multiple weather factors for the photovoltaic power short‐term forecasting publication-title: IET Renew. Power Gen. doi: 10.1049/iet-rpg.2016.1036 – volume: 182 issue: December 2019 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0010 article-title: A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid publication-title: Electr. Power Syst. Res. – ident: 10.1016/j.aej.2021.02.050_b0215 – volume: 216 start-page: 1 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0115 article-title: A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data publication-title: Energy Build. doi: 10.1016/j.enbuild.2020.109941 – volume: 163 start-page: 159 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0170 article-title: Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines publication-title: Knowl. Syst. doi: 10.1016/j.knosys.2018.08.027 – volume: 195 start-page: 222 year: 2017 ident: 10.1016/j.aej.2021.02.050_b0210 article-title: A short-term building cooling load prediction method using deep learning algorithms publication-title: Appl. Energy doi: 10.1016/j.apenergy.2017.03.064 – volume: 158 start-page: 2922 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0070 article-title: Short-term load forecasts using LSTM networks publication-title: Energy Procedia doi: 10.1016/j.egypro.2019.01.952 – volume: 22 start-page: 1345 issue: 10 year: 2010 ident: 10.1016/j.aej.2021.02.050_b0205 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2009.191 – volume: 9 start-page: 1735 year: 1997 ident: 10.1016/j.aej.2021.02.050_b0155 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 214 start-page: 118874 year: 2021 ident: 10.1016/j.aej.2021.02.050_b0225 article-title: A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting publication-title: Energy doi: 10.1016/j.energy.2020.118874 – volume: 8 start-page: 90 issue: 3 year: 2014 ident: 10.1016/j.aej.2021.02.050_b0090 article-title: Photovoltaic power forecasting using statistical methods: impact of weather data publication-title: IET Sci. Measur. Technol. doi: 10.1049/iet-smt.2013.0135 – volume: 62 start-page: 4029 issue: 10 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0120 article-title: Regularising LSTM classifier by transfer learning for detecting misogynistic tweets with small training set publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-020-01481-0 – volume: 196 start-page: 117127 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0080 article-title: Short-term load forecast using ensemble neuro-fuzzy model publication-title: Energy doi: 10.1016/j.energy.2020.117127 – volume: 194 start-page: 328 issue: 2019 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0110 article-title: Recurrent inception convolution neural network for multi short-term load forecasting publication-title: Energy Build. doi: 10.1016/j.enbuild.2019.04.034 – volume: 13 start-page: 1 issue: 886 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0125 article-title: Solving the cold-start problem in short-term load publication-title: Energies – volume: 103 start-page: 1040 year: 2015 ident: 10.1016/j.aej.2021.02.050_b0220 article-title: Day-ahead load forecast using random forest and expert input selection publication-title: Energy Convers. Manage. doi: 10.1016/j.enconman.2015.07.041 – volume: 20 start-page: 89 issue: 2 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0165 article-title: An efficient automated answer scoring system for Punjabi language publication-title: Egyptian Inform. J. doi: 10.1016/j.eij.2018.11.001 – volume: 7 start-page: 28309 year: 2019 ident: 10.1016/j.aej.2021.02.050_b0230 article-title: A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2901920 – volume: 122 start-page: 308 year: 2017 ident: 10.1016/j.aej.2021.02.050_b0100 article-title: load forecasting via deep neural networks publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2017.11.374 – volume: 20 start-page: 1 issue: 2668 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0130 article-title: Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building publication-title: Sensors – volume: 388 start-page: 110 year: 2020 ident: 10.1016/j.aej.2021.02.050_b0085 article-title: Empirical mode decomposition based multi-objective deep belief network for short-term power load forecasting publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.01.031 – ident: 10.1016/j.aej.2021.02.050_b0200 – volume: 7 start-page: 1 issue: 13 year: 2018 ident: 10.1016/j.aej.2021.02.050_b0175 article-title: An algorithm based on deep learning for predicting in-hospital cardiac arrest publication-title: J. Am. Heart Assoc. doi: 10.1161/JAHA.118.008678 |
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