Short-Term Load Forecasting for Commercial Building Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network with Similar Day Selection Model
Author:
Funder
Korea Institute of Energy Technology Evaluation and Planning
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42835-023-01660-3.pdf
Reference36 articles.
1. Kim D, Lee D, Nam H, Joo S (2022) Short-term load forecasting for commercial building using convolutional neural network (CNN) and long short-term memory (LSTM) model. ICEE2022 Conference, Seoul, Korea
2. Costa A, Keane M, Torrens J, Corry E (2013) Building operation and energy performance: Monitoring, analysis and optimisation toolkit. Appl Energy 101:310–316
3. Yalcinoz T, Eminoglu U (2005) Short term and medium term power distribution load forecasting by neural networks. Energy Convers Manage 46(9–10):1393–1405
4. Chitalia G, Pipattanasomporn M, Garg V, Rahman S (2020) Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. Appl Energy 278:1
5. Liu R, Chen T, Sun G, Muyeen SM, Lin S, Mi Y (2022) Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach. Electr Power Syst Res 206:1
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1. Short-Term Load Forecasting for Commercial Building Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network with Similar Day Selection Model;Journal of Electrical Engineering & Technology;2023-09-27
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