计算机科学
学习迁移
能源消耗
变压器
期限(时间)
电
机器学习
人工智能
数据挖掘
工程类
物理
量子力学
电压
电气工程
作者
Miguel López Santos,Sebastián Rubio García,Xela García-Santiago,Ana Ogando-Martínez,Fernando Echevarría Camarero,Gonzalo Blázquez Gil,Pablo Carrasco Ortega
标识
DOI:10.1016/j.enbuild.2023.113164
摘要
The use of deep learning for electrical demand forecasting has shown great potential in generating accurate results, but requires a large amount of data to train the models. However, the limited availability of electricity consumption information for new users or recently monitoring buildings in local energy communities make it difficult to achieve these good results. To address this challenge, this research study proposes a novel methodology based on the combination of the transfer learning (TL) concept and the temporal fusion transformer (TFT) architecture. This is an innovative approach used to transfer knowledge from a general pre-trained model based on the historical data of the community's buildings to new users, reducing the needs for large amounts of data for each new building. The results show that TFT provides more accurate load estimates than other state-of-the-art methods by reducing RMSE by more than 11%. Moreover, the TL approach improves the prediction of load demand of buildings with limited availability of historical data, reducing CV_RMSE, SMAPE, and WQLoss by more than 40% compared to models that do not use knowledge transfer. This is a significant improvement that facilitates the incorporation of new users to the community achieving accurate load estimations even with lack of data. Moreover, this approach leads to an optimized and cost-efficient planning and management of energy distribution in local energy communities.
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