Abstract
In this works, artificial neural network is combined with wavelet analysis for the forecast of solar irradiance. This method is characteristic of the preprocessing of sample data using wavelet transformation for the forecast, i.e., the data sequence of solar irradiance as the sample is first mapped into several time-frequency domains, and then a chaos optimization neural network is established for each domain. The forecasted solar irradiance is exactly the algebraic sum of all the forecasted components obtained by the respective networks, which correspond respectively the time-frequency domains. On the basis of combination of chaos optimization neural network and wavelet analysis, a model is developed for more accurate forecasts of solar irradiance. An example of the forecast of daily solar irradiance is presented in the paper, the historical daily records of solar irradiance in Shanghai constituting the data sample. The results of the example show that the accuracy of the method is more satisfactory than that of the methods reported before.
Highlights
The environmental conditions are an important factor in the performance of any photovoltaic module, PVM and in other fields, such as, air conditioning, the heating
This method is characteristic of the preprocessing of sample data using wavelet transformation for the forecast, i.e., the data sequence of solar irradiance as the sample is first mapped into several time-frequency domains, and a chaos optimization neural network is established for each domain
On the basis of combination of chaos optimization neural network and wavelet analysis, a model is developed for more accurate forecasts of solar irradiance
Summary
The environmental conditions are an important factor in the performance of any photovoltaic module, PVM and in other fields, such as, air conditioning, the heating. It is rather difficult to forecast accurately the behavior of solar irradiance by the traditional models, because they need the bases of the precise definition of problem domains as well as the identification of mathematical functions, but it is very difficult to define and identify precisely when systems are non-linear and there are parameters varying with time due to many factors. The control program often lacks the capability to adapt to the parameter changes. This is just the reason why most existing models were found with relatively big errors and sometimes difficult to use widely. Cao et al / Natural Science 1 (2009) 30-36 forecast of solar irradiance by means of the combination of artificial neural networks with wavelet analysis
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