Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting

Pingzhou Tang, Di Chen and Yushuo Hou

Chaos, Solitons & Fractals, 2016, vol. 89, issue C, 243-248

Abstract: As the world’s energy problem becomes more severe day by day, photovoltaic power generation has opened a new door for us with no doubt. It will provide an effective solution for this severe energy problem and meet human’s needs for energy if we can apply photovoltaic power generation in real life, Similar to wind power generation, photovoltaic power generation is uncertain. Therefore, the forecast of photovoltaic power generation is very crucial. In this paper, entropy method and extreme learning machine (ELM) method were combined to forecast a short-term photovoltaic power generation. First, entropy method is used to process initial data, train the network through the data after unification, and then forecast electricity generation. Finally, the data results obtained through the entropy method with ELM were compared with that generated through generalized regression neural network (GRNN) and radial basis function neural network (RBF) method. We found that entropy method combining with ELM method possesses higher accuracy and the calculation is faster.

Keywords: Entropy method; Extreme learning machine; Photovoltaic power generation forecasting (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:89:y:2016:i:c:p:243-248

DOI: 10.1016/j.chaos.2015.11.008

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