An ARMAX model for forecasting the power output of a grid connected photovoltaic system

被引:240
作者
Li, Yanting [1 ]
Su, Yan [2 ]
Shu, Lianjie [3 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Ind Engn & Logist Management, Shanghai 200030, Peoples R China
[2] Univ Macau, Dept Electromech Engn, Macau, Peoples R China
[3] Univ Macau, Fac Business, Macau, Peoples R China
关键词
Grid connection; Solar irradiance; Photovoltaic system; Efficiency; HOURLY SOLAR-RADIATION;
D O I
10.1016/j.renene.2013.11.067
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Power forecasting has received a great deal of attention due to its importance for planning the operations of photovoltaic (PV) system. Compared to other forecasting techniques, the ARIMA time series model does not require the meteorological forecast of solar irradiance that is often complicated. Due to its simplicity, the ARIMA model has been widely discussed as a statistical model for forecasting power output from a PV system. However, the ARIMA model is a data-driven model that cannot take the climatic information into account. Intuitively, such information is valuable for improving the forecast accuracy. Motivated by this, this paper suggests a generalized model, the ARMAX model, to allow for exogenous inputs for forecasting power output. The suggested model takes temperature, precipitation amount, insolation duration, and humidity that can be easily accessed from the local observatory as exogenous inputs. As the ARMAX model does not rely forecast on solar irradiance, it maintains simplicity as the conventional ARIMA model. On the other hand, it is more general and flexible for practical use than the ARIMA model. It is shown that the ARMAX model greatly improves the forecast accuracy of power output over the ARIMA model. The results were validated based on a grid-connected 2.1 kW PV system. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:78 / 89
页数:12
相关论文
共 35 条
  • [1] Predicting average energy conversion of photovoltaic system in Malaysia using a simplified method
    Alamsyah, TMI
    Sopian, K
    Shahrir, A
    [J]. RENEWABLE ENERGY, 2004, 29 (03) : 403 - 411
  • [2] Alan P.:., 1991, Forecasting with dynamic regression models
  • [3] Short-term power forecasting system for photovoltaic plants
    Alfredo Fernandez-Jimenez, L.
    Munoz-Jimenez, Andres
    Falces, Alberto
    Mendoza-Villena, Montserrat
    Garcia-Garrido, Eduardo
    Lara-Santillan, Pedro M.
    Zorzano-Alba, Enrique
    Zorzano-Santamaria, Pedro J.
    [J]. RENEWABLE ENERGY, 2012, 44 : 311 - 317
  • [4] [Anonymous], WORLD REN EN C SWED
  • [5] Online short-term solar power forecasting
    Bacher, Peder
    Madsen, Henrik
    Nielsen, Henrik Aalborg
    [J]. SOLAR ENERGY, 2009, 83 (10) : 1772 - 1783
  • [6] Box G.E.P., 1976, Time Series Analysis: Forecasting and Control
  • [7] Brockwell PJ., 1991, TIME SERIES THEORY M
  • [8] Brown, 1956, EXPONENTIAL SMOOTHIN
  • [9] Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis
    Cao, SH
    Cao, JC
    [J]. APPLIED THERMAL ENGINEERING, 2005, 25 (2-3) : 161 - 172
  • [10] Online 24-h solar power forecasting based on weather type classification using artificial neural network
    Chen, Changsong
    Duan, Shanxu
    Cai, Tao
    Liu, Bangyin
    [J]. SOLAR ENERGY, 2011, 85 (11) : 2856 - 2870