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| DOI | 10.1109/ACCESS.2024.3370442 |
| Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts | |
| 发表日期 | 2024 |
| ISSN | 2169-3536 |
| 起始页码 | 12 |
| 卷号 | 12 |
| 英文摘要 | This study aims to develop models for predicting hourly energy demand in the State of Connecticut, USA from 2011 to 2021 using machine learning algorithms inputted with airport weather stations' data from the Automated Surface Observing System (ASOS), demand data from ISO New England (ISO-NE). We built and evaluated nine different model experiments for each machine learning algorithm for each hour of the day addressing energy demand patterns, variations between workdays and weekends, and COVID-19 impacts. Error metrics analysis results highlighted that the GBR model demonstrated better performance compared to the MPR and RFR models. Incorporating both temporal and weather features in the models resulted in a noticeable improvement in error metrics. A consistent overestimation trend was observed for all models during the validation period (2018-2019) which may be attributed to energy efficiency measures and integration of behind-the-meter generation, with a further notable increase in overestimation following the onset of COVID-19 due to a change of habits during the pandemic in addition to decarbonization initiatives in the State. This study emphasizes the need for adapting models to dynamic consumption and weather patterns for improved grid management. |
| 英文关键词 | Electricity; Meteorology; Biological system modeling; Load modeling; Cooling; Resistance heating; Machine learning; Bayes methods; Energy consumption; Predictive models; Power system management; Power grids; COVID-19; Optimization methods; Energy demand; machine learning; weather stations; ISO New England; Bayesian optimization |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS记录号 | WOS:001176155700001 |
| 来源期刊 | IEEE ACCESS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/296482 |
| 作者单位 | University of Connecticut; University of Connecticut |
| 推荐引用方式 GB/T 7714 | . Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts[J],2024,12. |
| APA | (2024).Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts.IEEE ACCESS,12. |
| MLA | "Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts".IEEE ACCESS 12(2024). |
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