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

被引：1

作者：

Zhu, Yu-Rong ^{[1
]}

Wang, Jian-Guo ^{[1
]}

Sun, Yu-Qian ^{[1
]}

Wu, Jia-Jun ^{[1
]}

Zhao, Guo-Qiang ^{[1
]}

Yao, Yuan ^{[2
]}

Liu, Jian-Long ^{[3
]}

Chen, He-Lin ^{[4
]}

机构：

[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200072, Peoples R China

[2] Natl Tsing Hua Univ, Dept Chem Engn, Hsinchu 30013, Taiwan

[3] Shanghai Minghua Elect Power Sci & Technol Co Ltd, Shanghai 200092, Peoples R China

[4] Baoshan Iron & Steel Co Ltd, Ironmaking Plant, Shanghai 200941, Peoples R China

来源：

2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS | 2023年

关键词：

Combined cooling heating and power; short-term load forecasting; PSO-LSTM;

D O I：

10.1109/DDCLS58216.2023.10167106

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：639 / 644

页数：6

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