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1. Multi-objective optimization of crude oil-supply portfolio based on interval prediction data. (2022). Li, jianping ; Sun, Xiaolei ; Hao, Jun.
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2. Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study. (2022). Ciechulski, Tomasz ; Osowski, Stanislaw ; Szmurlo, Robert ; Siwek, Krzysztof.
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3. Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting. (2022). Zhao, Jinlou ; Zhang, Jindai.
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4. A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks. (2022). Jasiński, Tomasz ; Jasiski, Tomasz.
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5. A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China. (2022). Zhang, Zhiwei ; Zhou, Wenhao ; Li, Hailin.
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6. Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN). (2022). Tan, Mustafa ; Emeksiz, Cem.
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7. Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network. (2022). Liu, Yezheng ; Lu, Shixiang ; Xu, Qifa ; Jiang, Cuixia ; Kusiak, Andrew.
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8. A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India. (2022). Natarajan, Sukumar ; Chaturvedi, Shobhit ; McCullen, Nick ; Rajasekar, Elangovan.
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9. A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies. (2022). Onel, Onur ; Matthews, Logan R ; Pistikopoulos, Efstratios N ; Hallermann, Detlef R ; Floudas, Christodoulos A ; Niziolek, Alexander M ; Baratsas, Stefanos G ; Sorescu, Sorin M.
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10. Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting. (2021). Kusaf, Mehmet ; Salman, Diaa.
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11. A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV. (2021). Steens, Thomas ; Grottke, Matthias ; von Maydell, Karsten ; Telle, Jan-Simon ; di Modica, Gian-Luca ; Agert, Carsten ; Engel, Bernd ; Hanke, Benedikt.
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12. A deep learning framework for building energy consumption forecast. (2021). Somu, Nivethitha ; Ramamritham, Krithi ; Raman, Gauthama.
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13. Can electricity demand lead to air pollution? A spatio-temporal analysis of electricity demand with climatic conditions. (2021). Bedi, Jatin ; Toshniwal, Durga.
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14. Improved efficiency maximization strategy for vehicular dual-stack fuel cell system considering load state of sub-stacks through predictive soft-loading. (2021). Liu, Zhixiang ; Zeng, Tao ; Chan, Siew Hwa ; Wu, QI ; Deng, Chenghao ; Zhang, Caizhi.
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15. Energy consumption prediction model with deep inception residual network inspiration and LSTM. (2021). Salam, Abdulwahed ; el Hibaoui, Abdelaaziz.
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16. Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization. (2021). Ji, Zhengsen ; Xu, Xiaomin ; Li, Wanying ; Niu, Dongxiao ; Liu, DA.
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17. Real-time power prediction approach for turbine using deep learning techniques. (2021). Xie, Yonghui ; Xia, Xinlei ; Liu, Tianyuan ; Sun, Lei ; Zhang, DI.
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18. Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network. (2021). Yuan, Shan ; Cheng, Ming ; Cao, Junxing ; Wang, Jun.
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19. Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory. (2021). Lin, ZI ; Liu, Xiaolei.
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20. Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach. (2021). Zhao, Yu-Feng ; Pei, Ling-Ling ; Chen, Hai-Bao .
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21. A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19. (2021). Ma, Xin ; Lu, Hongfang.
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22. Analysis and forecast of Chinas energy consumption structure. (2021). Su, Bin ; Luo, Song ; Zeng, Sheng ; Tao, Qingmei ; Zhang, Minglong ; Liu, Jun ; Gao, Yuan.
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23. A hybrid deep learning-based online energy management scheme for industrial microgrid. (2021). Lu, Renzhi ; Wei, Min ; Sun, Mingyang ; Jiang, Junhui ; Zhang, Hai-Tao ; Xiao, Feng ; Ding, Yuemin ; Bai, Ruichang.
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24. Artificial Intelligence and emerging digital technologies in the energy sector. (2021). Lyu, Wenjing ; Liu, Jin.
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25. Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods. (2021). Chen, Yan ; Ahin, Utkucan ; Balli, Serkan.
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26. Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression. (2021). Chen, Ying ; Goude, Yannig ; Xu, Xiuqin ; Yao, Qiwei.
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27. Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting. (2021). Kumari, Pratima ; Toshniwal, Durga.
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28. A framework for predicting the production performance of unconventional resources using deep learning. (2021). Rui, Zhenhua ; Wang, Sen ; Feng, Qihong ; Javadpour, Farzam ; Qin, Chaoxu.
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29. An improved self-organizing incremental neural network model for short-term time-series load prediction. (2021). Wong, Yee Wan ; Rajkumar, Rajprasad Kumar ; Chong, Lee Wai ; Begam, Kasim Mumtaj.
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30. Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence. (2021). Sulbaran, Tulio ; Chaudhuri, Saptarshi ; Langar, Sandeep ; Baaaolu, Hakan ; Chakraborty, Debaditya ; Alam, Arafat.
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31. Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model. (2021). Safder, Usman ; Ifaei, Pouya ; Lim, Juin Yau ; Yoo, Changkyoo ; How, Bing Shen.
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32. Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network. (2021). Shi, YU ; Song, Xianzhi.
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33. A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity. (2020). Son, Hyojoo ; Kim, Changwan.
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34. Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting. (2020). Mariani, Viviana Cocco ; Fraccanabbia, Naylene ; Santos, Leandro Dos ; Sauer, Joo Guilherme ; Ribeiro, Gabriel Trierweiler.
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35. A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm. (2020). Sun, Xiaolei ; Feng, Qianqian ; Hao, Jun.
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36. Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting. (2020). Shang, Zhihao ; Yang, YI ; Chen, Yao.
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37. Deep Learning Approach to Power Demand Forecasting in Polish Power System. (2020). Ciechulski, Tomasz ; Osowski, Stanisaw.
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38. Multistep-ahead forecasting of coal prices using a hybrid deep learning model. (2020). Jianhua, Zhang ; Alameer, Zakaria ; Li, Kenli ; Ye, Haiwang ; Fathalla, Ahmed.
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39. Big data driven predictive production planning for energy-intensive manufacturing industries. (2020). Ge, Yuntian ; Zhang, Yingfeng ; Ma, Shuaiyin ; Li, Lin ; Lv, Jingxiang ; Yang, Haidong.
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40. A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting. (2020). Amraee, Turaj ; Kazemzadeh, Mohammad-Rasool ; Amjadian, Ali.
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41. Improving solar forecasting using Deep Learning and Portfolio Theory integration. (2020). , Paulo ; Anderson, Marcello ; Fernandez-Ramirez, Luis M.
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42. Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. (2020). Garg, Vishal ; Rahman, Saifur ; Pipattanasomporn, Manisa ; Chitalia, Gopal.
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43. Machine learning driven smart electric power systems: Current trends and new perspectives. (2020). Dong, Wei ; Yang, Qiang ; Ibrahim, Muhammad Sohail.
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44. Investigating the economics of the power sector under high penetration of variable renewable energies. (2020). Nagatomi, YU ; Matsuo, Yuhji ; Shibata, Yoshiaki ; Fujii, Yasumasa ; Endo, Seiya ; Komiyama, Ryoichi.
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45. A novel improved model for building energy consumption prediction based on model integration. (2020). Feng, Wei ; Lu, Shilei ; Wang, Ran.
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46. Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. (2020). Assimakopoulos, Vassilios ; Kourentzes, Nikolaos ; Petropoulos, Fotios ; Spiliotis, Evangelos.
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47. An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network. (2019). Hu, Zijian ; Zhong, Zhi ; Zhou, Suyang ; Jiang, Meng ; He, DI.
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48. Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection. (2019). Leigh, Seung-Bok ; Park, Kyungyong ; Lee, Joo Sang ; Jang, Jihoon ; Kim, Gahee ; Son, Eunjo.
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49. Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks. (2019). Hwangbo, Soonho ; Li, Qian ; Nam, Kijeon ; Rashidi, Jouan ; Loy-Benitez, Jorge ; Yoo, Changkyoo.
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50. Deep learning for multi-scale smart energy forecasting. (2019). Ahmad, Tanveer ; Chen, Huanxin.
    In: Energy.
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