Multi-load short-term prediction of an integrated energy system based on GAN-LSTM

作者：Junni Su, Fengchao Chen, Zejian Qiu, Zhiming Zhong, Huifang Zheng, Hua Zheng · 发表于：Journal of Physics Conference Series · 年份：2023 · DOI：10.1088/1742-6596/2564/1/012062 · 被引用次数：1 · 研究领域：Energy Load and Power Forecasting、Integrated Energy Systems Optimization、Electric Power System Optimization

Abstract An integrated energy system’s load consists of diverse forms, including electric load, cooling load, heat load, and more. Due to its strong randomness and volatility, traditional load forecasting methods are not suitable for the integrated energy system. To address the issue of short-term forecasting for multiple loads of the integrated energy system, the proposed paper utilizes generating adversarial networks (GAN). The proposed method analyzes the characteristics of comprehensive energy multi-load and combines meteorological factors with multi-load historical data to form the input dataset for the prediction model. To further improve the accuracy of the short-term forecasting of comprehensive energy multiple loads, the model employs a generator and a discriminator constructed based on the cyclic neural network of long- and short-term memory (LSTM). The example results demonstrate that the proposed method is effective in predicting multiple loads, with significantly better accuracy than traditional methods.