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  • A multivariate ensemble framework for short term solar photovoltaic output power forecast

    Author(s)
    Raza, Muhammad Qamar
    Nadarajah, Mithulananthan
    Ekanayake, Chandima
    Griffith University Author(s)
    Ekanayake, Chandima MB.
    Year published
    2017
    Metadata
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    Abstract
    In emerging renewable energy resources, solar photovoltaic (PV) is substantially important to fulfil the future electricity demand. One of the major challenges for large scale integration of PV into the grid is intermittent and uncertain nature of its output. Therefore, it is utmost important to forecast the solar PV output power with higher accuracy. In this paper, a novel ensemble forecast framework is proposed based on autoregressive (AR), radial basis function (RBF) and forward neural network (FNN) predictors. The neural predictor (FNN and RBF) are trained with particle swarm optimization (PSO) to enhance the prediction ...
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    In emerging renewable energy resources, solar photovoltaic (PV) is substantially important to fulfil the future electricity demand. One of the major challenges for large scale integration of PV into the grid is intermittent and uncertain nature of its output. Therefore, it is utmost important to forecast the solar PV output power with higher accuracy. In this paper, a novel ensemble forecast framework is proposed based on autoregressive (AR), radial basis function (RBF) and forward neural network (FNN) predictors. The neural predictor (FNN and RBF) are trained with particle swarm optimization (PSO) to enhance the prediction performance. Furthermore, wavelet transform (WT) technique is applied to remove the sharp spikes and fluctuations in it. In addition, correlated variables such as PV output power data, solar irradiance, temperature, humidity and wind speed are applied as inputs to multivariate ensemble network. The performance of proposed framework is analyzed for one day and week ahead case studies. The selected days from each season are a clear day (CD), partial cloudy day (PCD) and cloudy day (CLD). The proposed forecast framework provides a reduction in forecast nRMSE in seasonal daily and week ahead case studies.
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    Conference Title
    Proceedings of the 2017 IEEE Power & Energy Society General Meeting
    DOI
    https://doi.org/10.1109/PESGM.2017.8274676
    Subject
    Electrical and Electronic Engineering not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/377190
    Collection
    • Conference outputs

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