A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting

被引:164
作者
Ding, Song [1 ,2 ]
Li, Ruojin [1 ]
Tao, Zui [1 ]
机构
[1] Zhejiang Univ Finance & Econ, Sch Econ, Hangzhou 310018, Peoples R China
[2] Zhejiang Univ Finance & Econ, Ctr Reg Econ & Integrated Dev, Hangzhou 310018, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive discrete grey model; Genetic algorithm; Long-term forecasting; Photovoltaic power generation; NEURAL-NETWORK; PREDICTION; CONSUMPTION; STORAGE;
D O I
10.1016/j.enconman.2020.113644
中图分类号
O414.1 [热力学];
学科分类号
摘要
The rapidly growing photovoltaic power generation (PPG) instigates stochastic volatility of electricity supply that may compromise the power grid's stability and increase the grid imbalance cost. Therefore, accurate predictions of long-term PPG are of essential importance for the capacity deployment, plan improvement, consumption enhancement, and grid balance in systems with high penetration levels of PPG. Artificial neuron networks (ANNs) have been widely utilized to forecast the short-term PPG due to their strong nonlinear fitting competence that corresponds to the prerequisite for handling PPG samples characterized by volatility and nonlinearity. However, under the circumstances of the large time span, the insufficient data samples, and the periodicity existing in the long-term PPG datasets, the ANNs are easily stuck in overfitting and generate large forecasting deviations. Given this situation, a novel discrete grey model with time-varying parameters is initially designed to deal with various PPG time series featured with nonlinearity, periodicity, and volatility, which widely exist in the long-term PPG sequences. To be specific, improvements in this proposed model lie in the following aspects: first, the time-power item and periodic item are designated to compose the time-varying parameters to capture the nonlinear, periodic, and fluctuant developing trends of various time series. Second, owing to the complex nonlinear relationships between the above parameters and forecasting errors, the genetic algorithm applies shortcuts to seek optimum solutions and thereby enhances the prediction precision. Third, several practical properties of the proposed model are elaborated to further interpret the feasibility and adaptability of the proposed model. In experiments, a range of machine learning methods, autoregression models, and grey models are involved for comparisons to validate the feasibility and efficacy of the novel model, through the observations of the PPG in America and China. Finally, a superlative performance of the proposed model with the highest forecasting precision, small volatility of empirical results, and generalizability are confirmed by the aforementioned cases.
引用
收藏
页数:15
相关论文
共 56 条
[1]   Very short-term photovoltaic power forecasting with cloud modeling: A review [J].
Barbieri, Florian ;
Rajakaruna, Sumedha ;
Ghosh, Arindam .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 75 :242-263
[2]   Degradation prediction of proton exchange membrane fuel cell based on grey neural network model and particle swarm optimization [J].
Chen, Kui ;
Laghrouche, Salah ;
Djerdir, Abdesslem .
ENERGY CONVERSION AND MANAGEMENT, 2019, 195 :810-818
[3]   The US investment tax credit for solar energy: Alternatives to the anticipated 2017 step-down [J].
Comello, Stephen ;
Reichelstein, Stefan .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2016, 55 :591-602
[4]   Forecasting of photovoltaic power generation and model optimization: A review [J].
Das, Utpal Kumar ;
Tey, Kok Soon ;
Seyedmahmoudian, Mehdi ;
Mekhilef, Saad ;
Idris, Moh Yamani Idna ;
Van Deventer, Willem ;
Horan, Bend ;
Stojcevski, Alex .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 :912-928
[5]  
Deng J. L, 2002, GREY PREDICTION DECI
[6]   CONTROL-PROBLEMS OF GREY SYSTEMS [J].
DENG, JL .
SYSTEMS & CONTROL LETTERS, 1982, 1 (05) :288-294
[7]   Forecasting China's electricity consumption using a new grey prediction model [J].
Ding, Song ;
Hipel, Keith W. ;
Dang, Yao-guo .
ENERGY, 2018, 149 :314-328
[8]  
Ding Song, 2017, Control and Decision, V32, P1997, DOI 10.13195/j.kzyjc.2016.1022
[9]   A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output [J].
Dolara, Alberto ;
Grimaccia, Francesco ;
Leva, Sonia ;
Mussetta, Marco ;
Ogliari, Emanuele .
ENERGIES, 2015, 8 (02) :1138-1153
[10]   Optimal sizing of utility-scale photovoltaic power generation complementarily operating with hydropower: A case study of the world's largest hydro-photovoltaic plant [J].
Fang, Wei ;
Huang, Qiang ;
Huang, Shengzhi ;
Yang, Jie ;
Meng, Erhao ;
Li, Yunyun .
ENERGY CONVERSION AND MANAGEMENT, 2017, 136 :161-172