{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:05:43Z","timestamp":1767182743840,"version":"3.40.3"},"reference-count":46,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,17]]},"DOI":"10.1109\/icca62237.2024.10927804","type":"proceedings-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:16:27Z","timestamp":1743041787000},"page":"1-9","source":"Crossref","is-referenced-by-count":14,"title":["Advances and Evaluation of Intelligent Techniques in Short-Term Load Forecasting"],"prefix":"10.1109","author":[{"given":"Asif","family":"Ahamed","sequence":"first","affiliation":[{"name":"College of Engineering and Technology, Westcliff University,Irvine,California,USA"}]},{"given":"Nisher","family":"Ahmed","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, Westcliff University,Irvine,California,USA"}]},{"given":"Jamshaid Iqbal","family":"Janjua","sequence":"additional","affiliation":[{"name":"Al-Khawarizimi Institute of Computer Science (KICS), University of Engineering &#x0026; Technology (UET),Lahore,Pakistan"}]},{"given":"Zakir","family":"Hossain","sequence":"additional","affiliation":[{"name":"College of Engineering and Computer Science, California State University,Northridge,USA"}]},{"given":"Ekramul","family":"Hasan","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, Westcliff University,Irvine,California,USA"}]},{"given":"Tahir","family":"Abbas","sequence":"additional","affiliation":[{"name":"TIMES Institute,Depurtment of Computer Science,Multan,Pakistan"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/en15041295"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3060290"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115440"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3171270"},{"first-page":"2020","volume-title":"Renewable energy statistics","year":"2020","key":"ref5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116415"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2015.2438322"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.tej.2020.106884"},{"key":"ref9","first-page":"158928","article-title":"Industrial Ultra-Short-Term Load Forecasting With Data Completion","volume-title":"IEEE Access","volume":"8","author":"Jiang","year":"2020"},{"key":"ref10","first-page":"41578","article-title":"Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features","volume-title":"IEEE Access","volume":"11","author":"Rangelov","year":"2023"},{"key":"ref11","article-title":"A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons","volume-title":"Sensors (Basel, Switzerland)","volume":"23","author":"Giamarelos","year":"2023"},{"key":"ref12","article-title":"A hybrid model of modal decomposition and gated recurrent units for short-term load forecasting","volume-title":"PeerJ Computer Science","volume":"9","author":"Wang","year":"2023"},{"key":"ref13","first-page":"1","article-title":"A Selective Ensemble Approach for Accuracy Improvement and Computational Load Reduction in ANN-based PV power forecasting","volume-title":"IEEE Access","author":"Nespoli","year":"2022"},{"key":"ref14","first-page":"3425","article-title":"Geometric Deep-Learning-Based Spatiotemporal Forecasting for Inverter-Based Solar Power","volume-title":"IEEE Systems Journal","volume":"17","author":"Qin","year":"2023"},{"key":"ref15","first-page":"238","article-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"1976","journal-title":"San Francisco: Holden-Day"},{"key":"ref16","first-page":"1346","article-title":"Electricity consumption and production forecasting considering seasonal patterns: An investigation based on a novel seasonal discrete grey model","volume-title":"Journal of the Operational Research Society","volume":"74","author":"Zhou","year":"2022"},{"key":"ref17","first-page":"1","article-title":"Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting","volume-title":"J. Cases Inf. Technol.","volume":"24","author":"Yang","year":"2022"},{"key":"ref18","first-page":"77587","article-title":"A Short-Term Load Forecasting Model Based on Self-Adaptive Momentum Factor and Wavelet Neural Network in Smart Grid","volume-title":"IEEE Access","volume":"10","author":"Zulfiqar","year":"2022"},{"issue":"11","key":"ref19","first-page":"4132","article-title":"Short-term load forecasting method of neural network based on a new type of robust loss","volume":"44","author":"Qiuna","year":"2020","journal-title":"Power Grid Technology"},{"key":"ref20","first-page":"1217612","volume":"12176","author":"Han","year":"2022","journal-title":"Improved particle swarm optimization combined with least squares support vector machines for short-term load forecasting."},{"key":"ref21","first-page":"2427","article-title":"Short-term Load Forecasting Based on Multiple PSO-LSSVM under Electricity Market Environment","volume-title":"Journal of Physics: Conference Series","author":"Xu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107297"},{"key":"ref23","first-page":"2627","article-title":"Ultra-short-term wind power prediction based on double decomposition and LSSVM","volume-title":"Transactions of the Institute of Measurement and Control","volume":"45","author":"Qin","year":"2023"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115332"},{"key":"ref25","first-page":"8298","article-title":"Deep Learning-Based Forecasting Approach in Smart Grids With Microclustering and Bidirectional LSTM Network","volume-title":"IEEE Transactions on Industrial Electronics","volume":"68","author":"Jahangir","year":"2021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.03.081"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116328"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120480"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116177"},{"key":"ref30","article-title":"Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks","volume-title":"Sensors (Basel, Switzerland)","volume":"22","author":"Mahjoub","year":"2022"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120682"},{"key":"ref32","first-page":"82596","article-title":"A Novel Temporal Feature Selection Based LSTM Model for Electrical Short-Term Load Forecasting","volume-title":"IEEE Access","volume":"10","author":"Ijaz","year":"2022"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/2784563","article-title":"Research on Impulse Power Load Forecasting Based on Improved Recurrent Neural Networks","volume-title":"Computational Intelligence and Neuroscience","author":"Feng","year":"2022"},{"key":"ref34","first-page":"12719","volume-title":"Power load forecasting based on deep neural network","author":"Zhang","year":"2023"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118106"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2023.3250403"},{"key":"ref37","doi-asserted-by":"crossref","DOI":"10.3390\/en16031309","volume-title":"Multi Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model","author":"Aduama","year":"2023"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118874"},{"issue":"8","key":"ref39","first-page":"2885","article-title":"Ultra-short-term power load forecasting based on the double-layer XGBoost algorithm considering the influence of multiple features","volume":"47","author":"Chao","year":"2021","journal-title":"High Voltage Technology"},{"issue":"2","key":"ref40","first-page":"614","article-title":"Ultra-short-term power load forecasting based on the combined model of LSTM and XGBoost","volume":"44","author":"Zhenyu","year":"2020","journal-title":"Power Grid Technology"},{"issue":"5","key":"ref41","first-page":"46","article-title":"CNN-LST M-XGBoost short-term power loading forecasting method based on multi-model fusion","volume":"54","author":"Jiayi","year":"2021","journal-title":"China Electric Power"},{"key":"ref42","article-title":"Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar","volume-title":"Sensors (Basel, Switzerland)","volume":"23","author":"Zhang","year":"2023"},{"key":"ref43","first-page":"59754","article-title":"A Hybrid Short-Term Load Forecasting Model Based on Improved Fuzzy C-Means Clustering, Random Forest and Deep Neural Networks","volume-title":"IEEE Access","volume":"9","author":"Liu","year":"2021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106809"},{"key":"ref45","first-page":"60501","article-title":"A Data-Driven Approach to Predict Hourly Load Profiles From Time-of-Use Electricity Bills","volume-title":"IEEE Access","volume":"11","author":"Lazzeroni","year":"2023"},{"key":"ref46","first-page":"26","article-title":"Analysis of comprehensive energy development in the \u201c14th Five-Year Plan\u201d power plan","volume":"13","author":"Ming","year":"2020","journal-title":"China Electric Power Enterprise Management"}],"event":{"name":"2024 International Conference on Computer and Applications (ICCA)","start":{"date-parts":[[2024,12,17]]},"location":"Cairo, Egypt","end":{"date-parts":[[2024,12,19]]}},"container-title":["2024 International Conference on Computer and Applications (ICCA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10927736\/10927741\/10927804.pdf?arnumber=10927804","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:44:09Z","timestamp":1743061449000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10927804\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,17]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/icca62237.2024.10927804","relation":{},"subject":[],"published":{"date-parts":[[2024,12,17]]}}}