1. Investigation of building load prediction models based on integration of mechanism methods and data-driven models. (2025). Du-Ikonen, Liuliu ; Yuan, Xiaolei ; Lin, Xiaojie ; Zhang, Ning ; Zhong, Wei.
   In: Energy.
   RePEc:eee:energy:v:324:y:2025:i:c:s0360544225015750.

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2. An asynchronous electro-thermal coupling modeling method of lithium-ion batteries under dynamic operating conditions. (2025). Liu, Wei ; Teh, Jiashen ; Alharbi, Bader.
   In: Energy.
   RePEc:eee:energy:v:324:y:2025:i:c:s0360544225015324.

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3. MMEMformer: A multi-scale memory-enhanced transformer framework for short-term load forecasting in integrated energy systems. (2025). Qu, Bogang ; Zhao, Huirong ; Huang, Dongmei ; Peng, Daogang ; Wang, Danhao.
   In: Energy.
   RePEc:eee:energy:v:322:y:2025:i:c:s0360544225014045.

   Full description at Econpapers || Download paper

4. Wind power prediction based on improved self-attention mechanism combined with Bi-directional Temporal Convolutional Network. (2025). Lai, Ching-Ming ; Teh, Jiashen ; Shi, Jian.
   In: Energy.
   RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013088.

   Full description at Econpapers || Download paper

5. Integrated loads forecasting with absence of crucial factors. (2025). Lu, Xinhui ; Zhou, Kaile ; Hu, Rong.
   In: Energy.
   RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012745.

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6. ShuffleTransformerMulti-headAttentionNet network for user load forecasting. (2025). Yin, Linfei ; Ju, Linyi.
   In: Energy.
   RePEc:eee:energy:v:322:y:2025:i:c:s036054422501179x.

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7. A multi-energy loads forecasting model based on dual attention mechanism and multi-scale hierarchical residual network with gated recurrent unit. (2025). Lin, Chuan ; Rong, Fei ; Chen, Wenhao.
   In: Energy.
   RePEc:eee:energy:v:320:y:2025:i:c:s0360544225006176.

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8. Enhanced load forecasting for distributed multi-energy system: A stacking ensemble learning method with deep reinforcement learning and model fusion. (2025). Ren, Xiaoxiao ; Tian, Xin ; Wang, Kai ; Yang, Sifan ; Chen, Weixiong.
   In: Energy.
   RePEc:eee:energy:v:319:y:2025:i:c:s0360544225006735.

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9. Enhancing power system reliability through demand flexibility of Grid-Interactive Efficient Buildings: A thermal model-based optimization approach. (2025). Lukas, Lucija ; Panto, Milo.
   In: Applied Energy.
   RePEc:eee:appene:v:381:y:2025:i:c:s0306261924024292.

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10. Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer. (2024). Liu, Kun ; Yang, Haizhu ; Zhang, Tianyi.
    In: Energies.
    RePEc:gam:jeners:v:17:y:2024:i:20:p:5181-:d:1501102.

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11. A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building. (2024). Yao, Zhen ; Cheng, Ziwei.
    In: Energy.
    RePEc:eee:energy:v:312:y:2024:i:c:s0360544224032328.

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12. Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis. (2024). Qu, Bogang ; Zhao, Huirong ; Wang, Danhao ; Liu, YU ; Peng, Daogang.
    In: Energy.
    RePEc:eee:energy:v:308:y:2024:i:c:s0360544224025702.

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13. A novel time-series probabilistic forecasting method for multi-energy loads. (2024). Zhang, Zhisheng ; Ding, Yuhao ; Sun, Yuanyuan ; Xie, Xiangmin ; Fan, Jianhua.
    In: Energy.
    RePEc:eee:energy:v:306:y:2024:i:c:s0360544224022308.

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14. A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model. (2024). Zheng, Yong ; Chen, Haoyu ; Huang, Hai ; Yang, Bing.
    In: Applied Energy.
    RePEc:eee:appene:v:375:y:2024:i:c:s0306261924015496.

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15. A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory. (2024). Wu, Yongfei ; Alharthi, Yahya Z ; Dai, Shuangfeng ; Huang, Shoujun ; Mansouri, Seyed Amir ; Bagherzadeh, Leila.
    In: Applied Energy.
    RePEc:eee:appene:v:373:y:2024:i:c:s0306261924012510.

    Full description at Econpapers || Download paper

1. Arvanitidis, Athanasios Ioannis Enhanced short-term load forecasting using artificial neural networks. 2021 Energies. 14 7788-

2. Cai, Changchun Short-term electrical load forecasting based on VMD and GRU-TCN hybrid network. 2022 Appl Sci. 12 6647-
   Paper not yet in RePEc: Add citation now
   

3. Chen, Yongbao Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. 2017 Appl Energy. 195 659-670

4. Cui, Mianshan District heating load prediction algorithm based on bidirectional long short-term memory network model. 2022 Energy. 254 -

5. Ding, Yunfei A short-term wind power prediction model based on CEEMD and WOA-KELM. 2022 Renew Energy. 189 188-198

6. Ge, Quanbo Industrial power load forecasting method based on reinforcement learning and PSO-LSSVM. 2020 IEEE Trans Cybern. 52 1112-1124
   Paper not yet in RePEc: Add citation now
   

7. Guo, Fang Short-term electricity price forecasting based on the two-layer VMD decomposition technique and SSA-LSTM. 2022 Energies. 15 8445-

8. Guo, Xifeng Short-term household load forecasting based on Long-and Short-term Time-series network. 2021 Energy Rep. 7 58-64
   Paper not yet in RePEc: Add citation now
   

9. Huang, Shichao Short-term load forecasting based on the CEEMDAN-sample entropy-BPNN-transformer. 2022 Energies. 15 3659-

10. Jun, Wang ; Xia, Li ; Zhou, Xidong ; Kai, Zhang Ultra-short-term wind speed prediction based on VMD and LSTM [J]. 2020 Power System Protection and Control. 48 45-52
    Paper not yet in RePEc: Add citation now
    
11. Kiruthiga, D. ; Manikandan, V. Levy flight-particle swarm optimization-assisted BiLSTM+ dropout deep learning model for short-term load forecasting. 2023 Neural Comput Appl. 35 2679-2700
    Paper not yet in RePEc: Add citation now
    

12. Li, Guohui ; Wang, Siliang Sunspots time-series prediction based on complementary ensemble empirical mode decomposition and wavelet neural network. 2017 Math Probl Eng. 2017 -

13. Li, Ke Ultra-short term power load forecasting based on CEEMDAN-SE and LSTM neural network. 2023 Energy Build. 279 -
    Paper not yet in RePEc: Add citation now
    
14. Liang, Yi ; Niu, Dongxiao ; Hong, Wei-Chiang Short term load forecasting based on feature extraction and improved general regression neural network model. 2019 Energy. 166 653-663
    Paper not yet in RePEc: Add citation now
    
15. Liu, Haoming Short-term load forecasting of multi-energy in integrated energy system based on multivariate phase space reconstruction and support vector regression mode. 2022 Elec Power Syst Res. 210 -
    Paper not yet in RePEc: Add citation now
    

16. Liu, Hui Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. 2015 Appl Energy. 157 183-194

17. Niu, Dongxiao Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism. 2022 Appl Energy. 313 -

18. Niu, Dongxiao ; Wang, Yongli ; Wu, Desheng Dash Power load forecasting using support vector machine and ant colony optimization. 2010 Expert Syst Appl. 37 2531-2539
    Paper not yet in RePEc: Add citation now
    
19. Pei, Yuan Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model. 2021 Open Phys. 19 360-374
    Paper not yet in RePEc: Add citation now
    
20. Ran, Peng Short-term load forecasting based on ceemdan and transformer. 2023 Elec Power Syst Res. 214 -
    Paper not yet in RePEc: Add citation now
    

21. Shi, Jian ; Teh, Jiashen Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion. 2024 Appl Energy. 353 -

22. Tan, Zhongfu Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine. 2020 J Clean Prod. 248 -
    Paper not yet in RePEc: Add citation now
    

23. Wang, Shaomin Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics. 2020 Energy. 195 -

24. Wang, Shouxiang Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. 2019 Int J Electr Power Energy Syst. 109 470-479
    Paper not yet in RePEc: Add citation now
    
25. Xuan, Wang A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems. 2021 Int J Electr Power Energy Syst. 126 -
    Paper not yet in RePEc: Add citation now
    
26. Yang, Mao Ultra-short-term load prediction of integrated energy system based on load similar fluctuation set classification. 2023 Front Energy Res. 10 -
    Paper not yet in RePEc: Add citation now
    
27. Zhao, Xinyue A novel short‐term load forecasting approach based on kernel extreme learning machine: a provincial case in China. 2022 IET Renew Power Gener. 16 2658-2666
    Paper not yet in RePEc: Add citation now
    
28. Zhou, Dengji An electricity load forecasting model for Integrated Energy System based on BiGAN and transfer learning. 2020 Energy Rep. 6 3446-3461
    Paper not yet in RePEc: Add citation now
    

1. A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations. (2024). Tiwari, Prashant Kumar ; Srivastava, Mahima.
   In: Energy.
   RePEc:eee:energy:v:310:y:2024:i:c:s0360544224028524.

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2. Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model. (2024). Teh, Jiashen ; Shi, Jian ; Lai, Ching-Ming ; Alharbi, Bader.
   In: Energy.
   RePEc:eee:energy:v:297:y:2024:i:c:s0360544224010090.

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3. Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion. (2024). Teh, Jiashen ; Shi, Jian.
   In: Applied Energy.
   RePEc:eee:appene:v:353:y:2024:i:pb:s0306261923015106.

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4. Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq. (2023). Ates, Yavuz ; Bozkurt, Altug ; Majid, Ahmed Mazin.
   In: Energies.
   RePEc:gam:jeners:v:16:y:2023:i:6:p:2919-:d:1104238.

   Full description at Econpapers || Download paper

5. Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting. (2022). Alrasheedi, Abdullah ; Almalaq, Abdulaziz.
   In: Mathematics.
   RePEc:gam:jmathe:v:10:y:2022:i:15:p:2666-:d:874628.

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6. Entropy-Based Anomaly Detection in Household Electricity Consumption. (2022). Moure-Garrido, Marta ; Campo, Celeste ; Garcia-Rubio, Carlos.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:5:p:1837-:d:762362.

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7. Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting. (2022). Tsoukalas, Lefteri H ; Panapakidis, Ioannis P ; Kontogiannis, Dimitrios ; Bargiotas, Dimitrios ; Arvanitidis, Athanasios Ioannis ; Daskalopulu, Aspassia.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:4:p:1295-:d:746504.

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8. Methods of Forecasting Electric Energy Consumption: A Literature Review. (2022). Klyuev, Roman V ; Mengxu, QI ; Gavrina, Oksana A ; Martyushev, Nikita V ; Morgoev, Irbek D ; Efremenkov, Egor A ; Morgoeva, Angelika D.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:23:p:8919-:d:984103.

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9. Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods. (2022). Kumar, Venkata Pavan ; Venkata, Sivakavi Naga ; Refaat, Shady S ; Amir, Mohammad ; Padma, Kottala ; Pradeep, Darsy John ; Reddy, Challa Pradeep.
   In: Energies.
   RePEc:gam:jeners:v:15:y:2022:i:17:p:6124-:d:895783.

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10. Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models. (2020). Ceylan, Zeynep.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:39:y:2020:i:6:p:944-956.

    Full description at Econpapers || Download paper

11. 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. (2020). Guo, Yanhui ; Hong, Weichiang ; Zheng, Jiamei ; Fan, Guofeng.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:39:y:2020:i:5:p:737-756.

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12. A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity. (2020). Son, Hyojoo ; Kim, Changwan.
    In: Sustainability.
    RePEc:gam:jsusta:v:12:y:2020:i:8:p:3103-:d:344794.

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13. Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems. (2020). Lai, Loi Lei ; Pan, Keda ; Wang, Dongxiao ; Xie, Changhong.
    In: Forecasting.
    RePEc:gam:jforec:v:2:y:2020:i:4:p:25-487:d:438948.

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14. Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm. (2020). Yang, Hong-Tzer ; Aprillia, Happy ; Huang, Chao-Ming.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:8:p:1879-:d:344612.

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15. Event Effects Estimation on Electricity Demand Forecasting. (2020). Taniguchi, Rin-Ichiro ; Hori, Maiya ; Hirose, Kei ; Wada, Keigo.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:21:p:5839-:d:442360.

    Full description at Econpapers || Download paper

16. Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control. (2020). Fontan, Luis ; Martin, Fernando ; Rodriguez, Fermin ; Galarza, Ainhoa.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:19:p:5210-:d:424341.

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17. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. (2020). Elizondo-Gonzalez, Sergio ; Antonopoulos, Ioannis ; Kirli, Desen ; Flynn, David ; Couraud, Benoit ; Kiprakis, Aristides ; Robu, Valentin ; Wattam, Steve ; Norbu, Sonam.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:130:y:2020:i:c:s136403212030191x.

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18. Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits. (2020). Lu, Yujie ; Cui, Qingbin ; Shen, Meng ; Wei, Kua Harn.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:127:y:2020:i:c:s1364032120301337.

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19. Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions. (2020). Ahmad, Tanveer ; Zhang, Hongcai ; Huanxin, Chen.
    In: Energy.
    RePEc:eee:energy:v:198:y:2020:i:c:s036054422030390x.

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20. Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security. (2020). Zou, Shaohui ; Yang, Qing ; Zhang, Lei.
    In: Energy Policy.
    RePEc:eee:enepol:v:139:y:2020:i:c:s0301421520301178.

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21. An integrated method based on relevance vector machine for short-term load forecasting. (2020). Ping, Zuowei ; Vassiliadis, Vassilios S ; Wang, Maolin ; Ding, Jia ; Fu, Dongfei.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:287:y:2020:i:2:p:497-510.

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22. Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting. (2020). Li, Wenqiang ; Peng, Pei ; Chun, Liang ; Fan, Houhua ; Gong, Guangcai.
    In: Applied Energy.
    RePEc:eee:appene:v:270:y:2020:i:c:s0306261920306565.

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23. A novel improved model for building energy consumption prediction based on model integration. (2020). Feng, Wei ; Lu, Shilei ; Wang, Ran.
    In: Applied Energy.
    RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300738.

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24. A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM. (2019). Sun, Yonghui ; Zhou, Yan ; Wang, Sen ; Mahfoud, Rabea Jamil.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2019:i:1:p:87-:d:301203.

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25. Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting. (2019). Yang, Lintao.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:8:p:1433-:d:222669.

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26. A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand. (2019). Jin, Zhixin ; Yao, Xilong ; Zhao, Wenting ; Wang, Pan.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:7:p:1347-:d:220952.

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27. Aggregating Large-Scale Generalized Energy Storages to Participate in the Energy and Regulation Market. (2019). Yao, Yao ; Chen, Sijie ; Zhang, Peichao.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:6:p:1024-:d:214354.

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28. A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †. (2019). Bruno, Sergio ; la Scala, Massimo ; Dellino, Gabriella ; Meloni, Carlo.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:6:p:1006-:d:214090.

    Full description at Econpapers || Download paper

29. Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction. (2019). Zhao, Xin ; Zhang, Kanjian ; Yang, Jian ; Wei, Haikun.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:3:p:337-:d:199870.

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30. Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting. (2019). Yang, Hong-Tzer ; Aprillia, Happy ; Huang, Chao-Ming.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:24:p:4654-:d:295411.

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31. A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment. (2019). Meng, Zhaorui ; Xu, Xianze.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:24:p:4612-:d:294226.

    Full description at Econpapers || Download paper

32. Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia. (2019). Ni, Kailai ; Wang, Jianzhou ; Tang, Guangyu ; Wei, Danxiang.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:13:p:2467-:d:243188.

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33. A New Hybrid Approach to Forecast Wind Power for Large Scale Wind Turbine Data Using Deep Learning with TensorFlow Framework and Principal Component Analysis. (2019). Liu, Tianqi ; Khan, Mansoor ; Ullah, Farhan.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:12:p:2229-:d:239001.

    Full description at Econpapers || Download paper

34. Energy price prediction based on independent component analysis and gated recurrent unit neural network. (2019). He, Lulu ; Ye, Jimin ; Wei, Jian ; Jin, Haihong.
    In: Energy.
    RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319735.

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35. Quantification of electricity flexibility in demand response: Office building case study. (2019). Chen, Yongbao ; Li, Weilin ; Xu, Peng ; Sha, Huajing ; Hu, Chonghe ; Yang, Zhiwei.
    In: Energy.
    RePEc:eee:energy:v:188:y:2019:i:c:s0360544219317499.

    Full description at Econpapers || Download paper

36. Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept. (2019). Dev, Nalin Behari ; Barman, Mayur.
    In: Energy.
    RePEc:eee:energy:v:174:y:2019:i:c:p:886-896.

    Full description at Econpapers || Download paper

37. A new hybrid model to predict the electrical load in five states of Australia. (2019). Cui, Zhesen ; Wang, You-Gan ; Chen, Yanyan ; Wu, Jinran ; Kong, Demeng.
    In: Energy.
    RePEc:eee:energy:v:166:y:2019:i:c:p:598-609.

    Full description at Econpapers || Download paper

38. Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation. (2019). Mi, Zengqiang ; Wang, Tieqiang ; Dui, Neven ; Fotuhi-Firuzabad, Mahmoud ; Li, Kangping.
    In: Applied Energy.
    RePEc:eee:appene:v:253:y:2019:i:c:42.

    Full description at Econpapers || Download paper

39. A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources. (2019). Weng, Yang ; Luo, Shuman.
    In: Applied Energy.
    RePEc:eee:appene:v:242:y:2019:i:c:p:1497-1512.

    Full description at Econpapers || Download paper

40. Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data. (2019). Pertl, Michael ; Bindner, Henrik W ; Ziras, Charalampos ; Heinrich, Carsten.
    In: Applied Energy.
    RePEc:eee:appene:v:242:y:2019:i:c:p:1407-1421.

    Full description at Econpapers || Download paper

41. Short-term scenario-based probabilistic load forecasting: A data-driven approach. (2019). Pauwels, Eric J ; Khoshrou, Abdolrahman.
    In: Applied Energy.
    RePEc:eee:appene:v:238:y:2019:i:c:p:1258-1268.

    Full description at Econpapers || Download paper

42. Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load. (2019). Wang, Shengwei ; Tang, Rui ; Xu, Lei.
    In: Applied Energy.
    RePEc:eee:appene:v:237:y:2019:i:c:p:180-195.

    Full description at Econpapers || Download paper

43. Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market. (2019). Wang, Zhiwei ; Liang, Zheming ; Zhang, Xiaohu ; Diao, Ruisheng ; Shi, DI ; Bian, Desong.
    In: Applied Energy.
    RePEc:eee:appene:v:236:y:2019:i:c:p:916-926.

    Full description at Econpapers || Download paper

44. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques. (2019). Rahman, Saifur ; Cai, Mengmeng ; Pipattanasomporn, Manisa.
    In: Applied Energy.
    RePEc:eee:appene:v:236:y:2019:i:c:p:1078-1088.

    Full description at Econpapers || Download paper

45. Probabilistic individual load forecasting using pinball loss guided LSTM. (2019). Lu, Zongxiang ; Wang, YI ; Zhang, Ning ; Sun, Mingyang ; Gan, Dahua ; Kang, Chongqing.
    In: Applied Energy.
    RePEc:eee:appene:v:235:y:2019:i:c:p:10-20.

    Full description at Econpapers || Download paper

46. A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network. (2018). Zhan, Panpan ; Ma, Jian ; Zhang, Chunhong ; Tian, Chujie.
    In: Energies.
    RePEc:gam:jeners:v:11:y:2018:i:12:p:3493-:d:190634.

    Full description at Econpapers || Download paper

47. Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition. (2018). Bai, Yun ; Tao, Ying ; Yang, Shuai ; Li, Chuan.
    In: Energy.
    RePEc:eee:energy:v:165:y:2018:i:pb:p:1220-1227.

    Full description at Econpapers || Download paper

48. Deep belief network based k-means cluster approach for short-term wind power forecasting. (2018). Qi, Xiaoxia ; Wang, Kejun ; Song, Jiakang ; Liu, Hongda.
    In: Energy.
    RePEc:eee:energy:v:165:y:2018:i:pa:p:840-852.

    Full description at Econpapers || Download paper

49. Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques. (2018). Li, Shuyu ; Wang, Qiang.
    In: Energy.
    RePEc:eee:energy:v:161:y:2018:i:c:p:821-831.

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50. A deep learning model for short-term power load and probability density forecasting. (2018). Guo, Zhifeng ; Zhang, Xiaoling ; Yang, Shanlin ; Zhou, Kaile.
    In: Energy.
    RePEc:eee:energy:v:160:y:2018:i:c:p:1186-1200.

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51. A hybrid model based on selective ensemble for energy consumption forecasting in China. (2018). Li, Yuxi ; Xie, Ling ; Xiao, Jin ; Huang, Jing ; Liu, Dunhu.
    In: Energy.
    RePEc:eee:energy:v:159:y:2018:i:c:p:534-546.

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52. Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. (2018). Cyrino, Fernando Luiz ; de Oliveira, Erick Meira.
    In: Energy.
    RePEc:eee:energy:v:144:y:2018:i:c:p:776-788.

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53. Modelling urban energy requirements using open source data and models. (2018). Medjroubi, Wided ; Vogt, Thomas ; Agert, Carsten ; Alhamwi, Alaa.
    In: Applied Energy.
    RePEc:eee:appene:v:231:y:2018:i:c:p:1100-1108.

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54. 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. (2018). Dominguez, Servando Alvarez ; Diaz, Julian Arco ; Ramos, Jose Sanchez ; Montoya, Francisco Gil ; Garcia, David Hidalgo ; Guerrero, Carmen M.
    In: Applied Energy.
    RePEc:eee:appene:v:224:y:2018:i:c:p:538-549.

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55. Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model. (2018). Hong, Wei-Chiang ; Peng, Li-Ling ; Fan, Guo-Feng.
    In: Applied Energy.
    RePEc:eee:appene:v:224:y:2018:i:c:p:13-33.

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56. Energy consumption analysis of residential swimming pools for peak load shaving. (2018). Jing, Wei ; Zeng, Peng ; Yu, Haibin ; Song, Chunhe ; Rosenberg, Catherine.
    In: Applied Energy.
    RePEc:eee:appene:v:220:y:2018:i:c:p:176-191.

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57. Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines. (2017). Freire, Roberto Zanetti ; Santos, Leandro Dos ; Dos, Gerson Henrique.
    In: Energies.
    RePEc:gam:jeners:v:10:y:2017:i:8:p:1093-:d:105884.

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58. Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation. (2017). Kwak, Keun-Chang ; Yeom, Chan-Uk.
    In: Energies.
    RePEc:gam:jeners:v:10:y:2017:i:10:p:1613-:d:115200.

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59. The potential and usefulness of demand response to provide electricity system services. (2017). Lilliestam, Johan ; Aryandoust, Arsam.
    In: Applied Energy.
    RePEc:eee:appene:v:204:y:2017:i:c:p:749-766.

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