RESEARCH ON OPERATION PREDICTION OF DISTRIBUTED ENERGY SYSTEM BASED ON MULTIVARIATE NONLINEAR ANALYSIS

Feng Hongqing; Li Bowen; Zhang Liwei

doi:10.19912/j.0254-0096.tynxb.2022-0189

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (6) : 137-144. DOI: 10.19912/j.0254-0096.tynxb.2022-0189

RESEARCH ON OPERATION PREDICTION OF DISTRIBUTED ENERGY SYSTEM BASED ON MULTIVARIATE NONLINEAR ANALYSIS

Li Bowen¹, Zhang Liwei², Feng Hongqing²

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Abstract

In this paper, the multi-energy flow unstable energy consumption system is characterized as a multivariate nonlinear problem. The stability of the nonlinear energy system operation and the quality of output energy can be improved by short-term load forecasting. Firstly, the data mining of seven energy consumption related factors is carried out during neural network design, and then, the time domain rolling data prediction scheme is designed. The error of neural network is only 0.00143. The regression analysis of the optimized neural network topology, the input data and output data of the network training process are done. It gets R=0.99876, which indicates that the regression effect is significant, the data is credible. The research results are applied to the building load operation strategy, verified by biomass gas distributed energy system, the regression analysis between the observed data and the predicted data shows that R=0.999723, the regression effect is significant, which provides a high-precision prediction alternative way for the development of the operation strategy of the nonlinear energy consumption system.

Key words

energy utilization / neural networks / load forecasting / distributed energy / multiobjective optimization

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Li Bowen, Zhang Liwei, Feng Hongqing. RESEARCH ON OPERATION PREDICTION OF DISTRIBUTED ENERGY SYSTEM BASED ON MULTIVARIATE NONLINEAR ANALYSIS[J]. Acta Energiae Solaris Sinica. 2023, 44(6): 137-144 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0189

References

[1] 常圣强, 李望良, 张晓宇, 等. 生物质气化发电技术研究进展[J]. 化工学报, 2018, 69(8): 3318-3330.
CHANG S Q, LI W L, ZHANG X Y, et al.Progress in biomass gasification power generation technology[J]. CIESC journal, 2018, 69(8): 3318-3330.
[2] 苏鹏伟, 赵军, 邓帅, 等. 基于预测技术的建筑可再生能源系统匹配特性分析[J]. 太阳能学报, 2019, 40(8): 2360-2367.
SU P W, ZHAO J, DENG S, et al.Analysis of matching performance of building renewable energy system based on forecasting technology[J]. Acta energiae solaris sinica, 2019, 40(8): 2360-2367.
[3] 王歆宇. 燃气冷热电分布式能源系统运行分析及优化[D]. 北京: 北京建筑大学, 2019.
WANG X Y.Operation analysis and optimization of CCHP distributed energy system[D]. Beijing: Beijing University of Civil Engineering and Architecture, 2019.
[4] 白田田. 多能源互补的分布式冷热电联供系统的优化运行研究[D]. 北京: 华北电力大学, 2016.
BAI T T.Optimal operation of a complementary multi-energy combined cooling heating and power system[D]. Beijing: North China Electric Power University, 2016.
[5] MARTIN R, LAZAKIS I, BARBOUCHI S, et al.Sensitivity analysis of offshore wind farm operation and maintenance cost and availability[J]. Renewable energy, 2016, 85: 1226-1236.
[6] SHAFIEE M.Maintenance logistics organization for offshore wind energy: current progress and future perspectives[J]. Renewable energy, 2015, 77: 182-193.
[7] BROWN R H, VITULLO S R, CORLISS G F, et al.Detrending daily natural gas consumption series to improve short-term forecasts[C]//2015 IEEE Power & Energy Society General Meeting, Denver, CO, USA, 2015: 1-5.
[8] VAGHEFI A, JAFARI M A, BISSE E, et al.Modeling and forecasting of cooling and electricity load demand[J]. Applied energy, 2014, 136: 186-196.
[9] 茆美琴, 龚文剑, 张榴晨, 等. 基于EEMD-SVM方法的光伏电站短期出力预测[J]. 中国电机工程学报, 2013, 33(34): 17-24, 5.
MAO M Q, GONG W J, ZHANG L C, et al.Short-term photovoltaic generation forecasting based on EEMD-SVM combined method[J]. Proceedings of the CSEE, 2013, 33(34): 17-24, 5.
[10] 王飞, 米增强, 甄钊, 等. 基于天气状态模式识别的光伏电站发电功率分类预测方法[J]. 中国电机工程学报, 2013, 33(34): 75-82, 14.
WANG F, MI Z Q, ZHEN Z, et al.A classified forecasting approach of power generation for photovoltaic plants based on weather condition pattern recognition[J]. Proceedings of the CSEE , 2013, 33(34): 75-82, 14.
[11] GUO Q, TIAN Z, DING Y, et al.An improved office building cooling load prediction model based on multivariable linear regression[J]. Energy and buildings, 2015, 107: 445-455.
[12] LI Q, MENG Q L, CAI J J, et al.Applying support vector machine to predict hourly cooling load in the building[J]. Applied energy, 2009, 86(10): 2249-2256.
[13] 刘华财, 阴秀丽, 吴创之. 生物质气化发电能耗和温室气体排放分析[J]. 太阳能学报, 2015, 36(10): 2553-2558.
LIU H C, YIN X L, WU C Z.Energy consumption and greenhouse gas emission of biomass gasification and power generation system[J]. Acta energiae solaris sinica, 2015, 36(10): 2553-2558.
[14] 邹涛, 丁宝苍, 张端. 模型预测控制工程应用导论[M]. 北京: 化学工业出版社, 2010.
ZOU T, DING B C, ZHANG D.MPC: an introduction to industrial applications[M]. Beijing: Chemical Industry Press, 2010.
[15] HAGAN M T, DEMUTH H B, BEALE M H.Neural network design[M]. Beijing: China Machine Press, 2002.