PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy

被引:81
作者
Semero, Yordanos Kassa [1 ]
Zhang, Jianhua [1 ]
Zheng, Dehua [2 ]
机构
[1] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China
[2] Goldwind Sci & Technol Co Ltd, Beijing 100176, Peoples R China
来源
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS | 2018年 / 4卷 / 02期
关键词
ANFIS; binary genetic algorithm; feature selection; hybrid method; particle swarm optimization; PV power forecasting; ARTIFICIAL NEURAL-NETWORK; WIND-SPEED; HYBRID APPROACH; OUTPUT; PREDICTION; MODEL;
D O I
10.17775/CSEEJPES.2016.01920
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic (PV) installations. An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation. A day-ahead, hourly mean PV power generation forecasting method based on a combination of genetic algorithm (GA), particle swarm optimization (PSO) and adaptive neuro-fuzzy inference systems (ANFIS) is presented in this study. Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant; and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant. The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing. Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches. The proposed approach outperformed existing artificial neural network (ANN), linear regression (LR), and persistence based forecasting models, validating its effectiveness.
引用
收藏
页码:210 / 218
页数:9
相关论文
共 38 条
[21]   Parameter Optimization Using PSO for Neural Network-Based Short-Term PV Power Forecasting in Indian Electricity Market [J].
Yadav, Harendra Kumar ;
Pal, Yash ;
Tripathi, M. M. .
PROCEEDINGS OF RECENT INNOVATIONS IN COMPUTING, ICRIC 2019, 2020, 597 :331-348
[22]   A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service [J].
Jeong, Jinhwa ;
Lee, Dongkyu ;
Chae, Young Tae .
ENERGIES, 2023, 16 (22)
[23]   Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data [J].
Wolff, Bjoern ;
Kuehnert, Jan ;
Lorenz, Elke ;
Kramer, Oliver ;
Heinemann, Detlev .
SOLAR ENERGY, 2016, 135 :197-208
[24]   Gene selection using Gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy [J].
Mao, Yong ;
Zhou, Xiaobo ;
Yin, Zheng ;
Pi, Daoying ;
Sun, Youxian ;
Wong, Stephen T. C. .
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, PROCEEDINGS, 2006, 4062 :799-806
[25]   Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change [J].
Atuahene, Samuel ;
Bao, Yukun ;
Ziggah, Yao Yevenyo ;
Gyan, Patricia Semwaah ;
Li, Feng .
ENERGIES, 2018, 11 (10)
[26]   An hierarchical approach for automatic segmentation of leaf images with similar background using kernel smoothing based Gaussian process regression [J].
Brindha, Jaya G. ;
Gopi, E. S. .
ECOLOGICAL INFORMATICS, 2021, 63
[27]   A novel deep learning approach for short-term wind power forecasting based on infinite feature selection and recurrent neural network [J].
Shao, Haijian ;
Deng, Xing ;
Jiang, Yingtao .
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2018, 10 (04)
[28]   A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series [J].
Karasu, Seckin ;
Altan, Aytac ;
Bekiros, Stelios ;
Ahmad, Wasim .
ENERGY, 2020, 212 (212)
[29]   Soft Sensor method of Mill Load for Grinding Process based on GA-PLS from Spectral Data using Feature Selection [J].
Tang Jian ;
Zhao Li-Jie ;
Yue Heng ;
Chai Tian-You .
PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE, 2010, :5066-5071
[30]   Two-Stage Hybrid Feature Selection Approach Using Levy's Flight Based Chicken Swarm Optimization for Stock Market Forecasting [J].
Verma, Satya ;
Prakash Sahu, Satya ;
Prasad Sahu, Tirath .
COMPUTATIONAL ECONOMICS, 2024, 63 (06) :2193-2224