Study Of Methodology Of Solar Irradiance Forecasting Using Compound Forecast Techniques On The Basis Of Neural Network

With the increasing problem of world "Energy Crisis", today, every country all over the world are emphasizing on the research work of energy conservation and new energy development, teeth and nail. Solar energy is a representative new energy which is abundant and clean. As the technology of utilization of solar energy develops, the need of solar irradiance data are increasing, the demand of precious are heightening, and the technique of forecast solar irradiance intensity in future are requiring further improving. Solar irradiance forecast can fill in the irradiance database and provide an important tool for solar energy application and new energy utilization. Meanwhile, an appropriate solar irradiance model is a key precondition of accurate load forecast for air conditioning systems. This paper aims to the deep research into the theories and methods of solar irradiance forecast.Solar irradiance is of non-linear characteristic. Neural networks are skilled in handling non-linear problems. Because of the neural networks' capability of non-linear function approximation and self-study, self-adapt, it becomes the main research approach of this paper. Considering the monotony of Sigmoid function, which is the activation function of neural network, this paper replaced the Sigmoid function with Morlet wavelet function, namely, wavelet neural network. Wavelet neural network combines the prominent ability of wavelet functions in time-frequency domains multi-resolution analyses and the good performance of neural networks in approximating nonlinear functions. Two new variables were introduced in Wavelet neural network, viz. dilation factor and translation factor, which entitles the network more degree of freedom, then the network, can approximate the function more appropriately and effectively. Considering the characteristic of non-linear and time-varying of solar irradiance, a feedback layer was introduced in the hidden layer of back-propagation network. The feedback layer functions, as a one-step delay operator for memorizing, which, accordingly, entitles system the function of adapting time-varying characteristic. This self-connection method enables the network to have sensitivity to historical data, and inner feedback network enhances the network capability of handling dynamic information, which can describe the characteristic of dynamic system directly and achieve the target of dynamic modeling. According to the analysis above, a diagonal recurrent wavelet back propagation neural network model for solar irradiance forecast was built.Considering the advantages of neural networks, slow convergence and local minimum trend, the improvement of training algorithm and the updating method of the weights and biases of network were proposed: (1). Compared existing modified training algorithms and chose the adaptive variable-step back-propagation algorithm that has momentum terms to train network, which overcame the problems of slow convergence and local minimum trend effectively. (2). Contacted the selection of initial values with learning data sample, with the attempt to avoid the uncertainty which brought by random initializing. According to the periodicity of learning data sample, combining with batch training mode, a method of batch-average-weight was put forward. The batch-average-weight method can ensure the optimal initial values and excellent training result every time.Besides, considering the importance of the training sample data of the network, the influence factors of the solar irradiance should be analyzed carefully to eliminate dependent factors and determine independent factors. On one hand, implemented correlation analysis to historical solar irradiance data, the main modeling parameter, analyzed the inner changing rule of the time sequence and choose the most correlative historical data of the forecasting day or forecasting hour as the input information of the network. On the other hand, the fluctuation of solar irradiance intensity is a typical non-linear curve which is dynamic, time-varying and multi- interfering. Among the influence factor of solar irradiance, the stochastic factors, such as cloud coverage and atmosphere condition are very important. This paper introduced the weather forecast information about cloud to the neural network model. The weather forecast information were fuzzificated and modified as the important input of the network, thus, the network could learn the connection between solar irradiance and air condition adequately, the result of emulating forecast could reflect the sudden influence caused by weather change actively. In a word, the proposed model achieves higher forecasting accuracy.According to the analysis above, the achievements of this paper was a complete set of models of forecasting solar irradiance, including daily total solar irradiance forecast model, hourly total solar irradiance forecast model, daily diffuse solar irradiance forecast model, hourly diffuse solar irradiance forecast model. This paper took the correlative data of Macao from 1991 to 2000 and Shanghai from 2001 to 2002 as the investigate object of the forecast models. The performance parameters, relative errors and absolute errors of tracing forecast and emulating forecast were analyzed according to the example. In order to further assess the goodness of the forecast model, several traditional models are used to calculate or predict solar irradiance for comparing with the four diagonal recurrent wavelet back propagation neural network forecast models, the results demonstrate that the root mean square errors and the mean relative errors are improved significantly, and the coefficient of determinations are higher than 0.93.