Logo image
Short-term Load Forecasting of CCHP System Based on PSO-LSTM
Conference paper

Short-term Load Forecasting of CCHP System Based on PSO-LSTM

Yu-Rong Zhu, Jian-Guo Wang, Yu-Qian Sun, Jia-Jun Wu, Guo-Qiang Zhao, Yuan Yao, Jian-Long Liu and He-Lin Chen
Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023, pp.639-644
2023

Abstract

Combined cooling heating and power PSO-LSTM short-term load forecasting Artificial Intelligence Computer Science Applications Control and Optimization
With the inherent need to accelerate the high-quality development of China's economy, it is necessary to build a clean, low-carbon, safe and efficient modern energy system. The traditional energy system is centralized and large-scale, and the transmission and distribution system are complex, with low adaptability and reliability. The Combined cooling, heating and power system has been widely promoted and concerned for its advantages of improving energy efficiency, saving energy and reducing emissions. This paper takes the Combined cooling, heating and power system of Shanghai Qiantan Energy Station as the research object and establishes a load prediction model on the user side. This paper first introduces the Combined cooling, heating and power system of Shanghai Qiantan Energy Station, then explores the influencing factors of load data, builds the PSO-LSTM model and analyzes the prediction results, and finally draws a conclusion.

Metrics

1 Record Views

Details

Logo image