Journal of Shanghai Jiaotong University >
Boiler Load Forecasting of CHP Plant Based on Attention Mechanism and Deep Neural Network
Received date: 2021-09-08
Accepted date: 2021-11-10
Online published: 2022-11-24
Accurate boiler load forecasting of cogeneration units plays a direct role in production management and dispatching of power plants. A long-term load forecasting model of combined heat and power (CHP) based on attention mechanism and the deep convolution long-short-term memory network (CNN-LSTM-AM) is proposed, which takes the historical data of boiler outlet steam flow (load) and multi-dimensional load influence factors as input to make long-term load forecasting. First, the original data is screened by Pearson correlation coefficient judgment. Then the processed data is processed by convolution layer for feature extraction and further dimensionality reduction, fitted through long-term and short-term memory layer, and optimized the weight by adopting attention mechanism, so as to achieve accurate load forecasting. The proposed model is verified by the measured data of Tongxiang Power Plant in Zhejiang Province. The results show that the MAPE of the proposed method is less than 1%. It can realize the accurate prediction of boiler load, which has a certain reference significance for the application of intelligent algorithm in the field of combined heat and power.
WAN Anping, YANG Jie, MIAO Xu, CHEN Ting, ZUO Qiang, LI Ke . Boiler Load Forecasting of CHP Plant Based on Attention Mechanism and Deep Neural Network[J]. Journal of Shanghai Jiaotong University, 2023 , 57(3) : 316 -325 . DOI: 10.16183/j.cnki.jsjtu.2021.346
[1] | 陈向国. 智慧供热引领供热行业发展新方向[J]. 节能与环保, 2021(3): 22-25. |
[1] | CHEN Xiangguo. Smart heating leads the new development direction of heating industry[J]. Energy Conservation & Environmental Protection, 2021 (3): 22-25. |
[2] | 陈新和, 裴玮, 邓卫, 等. 数据驱动的虚拟电厂调度特性封装方法[J]. 中国电机工程学报, 2021, 41(14): 4816-4828. |
[2] | CHEN Xinhe, PEI Wei, DENG Wei, et al. Data-driven virtual power plant dispatching characteristic packing method[J]. Proceedings of the CSEE, 2021, 41(14): 4816-4828. |
[3] | 许可. 母管制热电机组热力系统建模与负荷优化分配[D]. 杭州: 浙江大学, 2020. |
[3] | XU Ke. Thermal system modeling of main-pipeline cogeneration unit and combined heat and power optimized distribution[D]. Hangzhou: Zhejiang University, 2020. |
[4] | DUDZIK W, NALEPA J, KAWULOK M. Evolving data-adaptive support vector machines for binary classification[J]. Knowledge-Based Systems, 2021, 227: 107221. |
[5] | YANG J, ZHANG T Z, HONG J C, et al. Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle[J]. Energy, 2021, 233: 121221. |
[6] | IMANI M. Electrical load-temperature CNN for residential load forecasting[J]. Energy, 2021, 227: 120480. |
[7] | KUMAR D, MATHUR H D, BHANOT S, et al. Forecasting of solar and wind power using LSTM RNN for load frequency control in isolated microgrid[J]. International Journal of Modelling and Simulation, 2021, 41(4): 311-323. |
[8] | REZAEE M J, DADKHAH M, FALAHINIA M. Integrating neuro-fuzzy system and evolutionary optimization algorithms for short-term power generation forecasting[J]. International Journal of Energy Sector Management, 2019, 13(4): 828-845. |
[9] | KARABIBER A, AL?IN ? F. Short term PV power estimation by means of extreme learning machine and support vector machine[C]// 2019 7th International Istanbul Smart Grids and Cities Congress and Fair. Istanbul, Turkey: IEEE, 2019: 41-44. |
[10] | TAN Z F, DE G, LI M L, et al. Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine[J]. Journal of Cleaner Production, 2020, 248: 119252. |
[11] | ZYME?KA P, SZEGA M. Short-term scheduling of gas-fired CHP plant with thermal storage using optimization algorithm and forecasting models[J]. Energy Conversion and Management, 2021, 231: 113860. |
[12] | 刘亚珲, 赵倩. 基于聚类经验模态分解的CNN-LSTM超短期电力负荷预测[J]. 电网技术, 2021, 45(11): 4444-4451. |
[12] | LIU Yahui, ZHAO Qian. Ultra-short-term power load forecasting based on cluster empirical mode decomposition of CNN-LSTM[J]. Power System Technology, 2021, 45(11): 4444-4451. |
[13] | 陆继翔, 张琪培, 杨志宏, 等. 基于CNN-LSTM混合神经网络模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(8): 131-137. |
[13] | LU Jixiang, ZHANG Qipei, YANG Zhihong, et al. Short-term load forecasting method based on CNN-LSTM hybrid neural network model[J]. Automation of Electric Power Systems, 2019, 43(8): 131-137. |
[14] | OKAMURA H, OSADA Y, NISHIJIMA S, et al. Novel robust time series analysis for long-term and short-term prediction[J]. Scientific Reports, 2021, 11: 11938. |
[15] | FENG G L, ZHANG L Y, YANG J H, et al. Long-term prediction of time series using fuzzy cognitive maps[J]. Engineering Applications of Artificial Intelligence, 2021, 102: 104274. |
[16] | ZHANG G, BAI X Q, WANG Y X. Short-time multi-energy load forecasting method based on CNN-Seq2Seq model with attention mechanism[J]. Machine Learning With Applications, 2021, 5: 100064. |
[17] | JIN Y L, TAN E L, LI L, et al. Hybrid traffic forecasting model with fusion of multiple spatial toll collection data and remote microwave sensor data[J]. IEEE Access, 2018(6): 79211-79221. |
[18] | YANG Y R, XIONG Q Y, WU C, et al. A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism[J]. Environmental Science and Pollution Research International, 2021, 28(39): 55129-55139. |
[19] | BOMMISETTY R M, PRAKASH O, KHARE A. Keyframe extraction using Pearson correlation coefficient and color moments[J]. Multimedia Systems, 2020, 26(3): 267-299. |
[20] | CHEN Q, ZHANG W Y, ZHU K, et al. A novel trilinear deep residual network with self-adaptive Dropout method for short-term load forecasting[J]. Expert Systems With Applications, 2021, 182: 115272. |
[21] | 张珂, 杨歆豪, 张嘉慧, 等. 基于高次指数平滑动态边界限制的深度学习优化算法[J]. 信息与控制, 2021, 50(6): 685-693. |
[21] | ZHANG Ke, YANG Xinhao, ZHANG Jiahui, et al. Deep learning optimization algorithm based on high order exponential smoothing dynamic boundary constraint[J]. Information and Control, 2021, 50(6): 685-693. |
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