Search

In this paper, an improved complex-valued multi-layer perceptron (ICMLP) network has been proposed with a view to forecast the fluctuating prices of the deregulated market. The existing literature focuses on a hybrid of hard and soft computing models that should be able to capture the nonlinearity associated with those electricity market prices. Apart from individual models, the hybrid algorithms are based on the integration of different computing paradigms, which in turn increases the time complexity. An individual model for estimating electricity prices has yet to be developed. This paper proposes an exclusive approach called ICMLP with logarithmic performance index for forecasting electricity prices. In ICMLP, a novel optimization criterion is used that takes into account minimizing both the errors by magnitude and phase. Efficacy of ICMLP is evaluated using benchmark data sets of Spanish electricity market. The results have been found to be promisingly better than the existing prediction-based models. Simulation results demonstrate that the ICMLP is superior in terms of accuracy, training speed, and structure compactness. [ABSTRACT FROM AUTHOR]

Day-ahead electricity price forecasting is still an open problem in electricity markets. One major method is used in solving this problem is artificial neural networks (ANN). But they are usually trained slowly and need large numbers of patterns. NN trained using Levenberg-Marquardt (LM) learning is proposed and partial autocorrelation is applied on time series data to get correct input values. The functionality of the NN-LM is higher than the traditional ANN and some other hybrid approaches. To show the effectiveness and accuracy of the NN-LM method, the Indian and the Austrian energy exchange markets are considered. It is significant to note that for the very first time, the NN-LM based approach is being tested on both the energy markets. Finally, the flexibility of the proposed approach is checked using a 4-fold cross-validation technique. The 4-fold cross-validation strategy is capable of improving the generalization ability of the model and accomplishing higher forecast precision. [ABSTRACT FROM AUTHOR]