KIEE - The Transactions of the Korean Institute of Electrical Engineers

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C. Chatuanramtharnghaka, Y. Aoyagi, M. Z. A. Ab Kadir, 2024, Review of demand response programs in the context of renewable energy and electric vehicles, World Electric Vehicle Journal, Vol. 15, No. 9, pp. 412

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2025, Demand Flexibility: The Value and Participation Mechanisms

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J. Jiang, Y. Gao, F. Wen, 2020, Smart charging strategies for electric vehicles to enhance power system flexibility, Journal of Cleaner Production, Vol. 278, pp. 123-136

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S. D. Braithwait, 2010, Accuracy of Alternative Baseline Methods for Measuring Demand Response Program Impacts, pp. 1-18

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O. Valentini, N. Andreadou, P. Bertoldi, A. Lucas, I. Saviuc, E. Kotsakis, 2022, Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load, Energies, Vol. 15, No. 14

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X. Wang, W. Tang, 2020, Analysis and Evaluation of Baseline Manipulation in Demand Response Programs, arXiv preprint arXiv:2011.10681

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H. G. R. Harikrishnan, S. Sreedharan, N. B. C., 2025, Advanced short‑term load forecasting for residential demand response: An XGBoost‑ANN ensemble approach, Electric Power Systems Research, Vol. 242

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Y. Wang, S. Sun, X. Zeng, Y. Kong, J. Chen, 2021, Short‑term load forecasting of industrial customers based on TCN‑LightGBM, IEEE Transactions on Power Systems, Vol. 36, No. X, pp. 1984-1997

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A. Gassar, 2024, Short‑Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning, Buildings, Vol. 14, No. 7

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D. Zhou, M. Balandat, C. Tomlin, 2016, Residential Demand Response Targeting Using Machine Learning with Observational Data, arXiv preprint arXiv:1607.00595

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X. Wang, W. Tang, 2020, Analysis and Evaluation of Baseline Manipulation in Demand Response Programs, arXiv preprint arXiv:2011.10681

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2022, Demand response performance and uncertainty: A systematic literature review, Elsevier Energy Research & Social Science

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O. Valentini, N. Andreadou, P. Bertoldi, A. Lucas, I. Saviuc, E. Kotsakis, 2022, Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load, Energies, Vol. 15, No. 14

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Sae-Hong Kim, Joon-Hee Lee, Seung-Ki Sul, Dong-Hwa Park, 2014, A Framework for Baseline Load Estimation in Demand Response, pp. 644-649

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2008, Demand Response Measurement &Verification

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A. A. A. Gassar, 2024, Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning, Buildings, Vol. 14, No. 7

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Y. Roy, A. Ishmam, K. A. Taher, 2021, Demand Forecasting in Smart Grid Using Long Short‑Term Memory, arXiv preprint arXiv:2107.13653

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X. Zhang, F. Gao, L. Xu, 2022, A Prophet‑LSTM Hybrid Model for Peak Load Forecasting in Distribution Networks, Energies, Vol. 15, No. 15

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Margaux Brégère, Ricardo J. Bessa, 2020, Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders, arXiv preprint

20

Haris Mansoor, Sarwan Ali, Imdadullah Khan, Naveed Arshad, Muhammad Asad Khan, Safiullah Faizullah, 2019, Short-Term Load Forecasting Using AMI Data (FMF), arXiv preprint

21

Datong Zhou, Maximilian Balandat, Claire Tomlin, 2016, Residential Demand Response Targeting Using Machine Learning with Observational Data, arXiv preprint

22

Yang Xu, Weijun Gao, Fanyue Qian, 2021, Potential Analysis of the Attention-Based LSTM Model in Ultra‑Short‑Term Forecasting of Building HVAC Energy Consumption, Frontiers in Energy Research

23

Ke Hua, Zhichao Wang, Feng Zuo, Qihang Wang, 2022, Short-Term Electricity Load Forecasting Method Based on Attention and Time Series Forecasting

24

Sheng Ding, Dongyi He, Guiran Liu, 2024, Improving Short-Term Load Forecasting with Multi-Scale Convolutional Neural Networks and Transformer-Based Multi-Head Attention Mechanisms, Electronics, Vol. 13, No. 24

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Shuaishuai Li, Weizhen Chen, 2025, A Study on Interpretable Electric Load Forecasting Model with Spatiotemporal Feature Fusion Based on Attention Mechanism, Technologies, Vol. 13, No. 6

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Jing Xiong, Yu Zhang, 2023, A Unifying Framework of Attention‑Based Neural Load Forecasting, IEEE Access

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Chunrui Lei, Heng Zhang, Zhigang Wang, Qiang Miao, 2024, Multi-Model Fusion Demand Forecasting Framework Based on Attention Mechanism, Processes, Vol. 12, No. 11

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S. Hochreiter, J. Schmidhuber, 1997, Long short-term memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780

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K. Cho, B. van Merrienboer, C. Gulcehre, 2014, Learning phrase representations using RNN encoder-decoder for statistical machine translation, pp. 1724-1734

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Y. Kong, J. Kim, Y. Lee, 2017, Short-term electric load forecasting based on long short-term memory recurrent neural network, Energies, Vol. 10, No. 1, pp. 1-20

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers