Comparative analysis of self organizing maps vs. multilayer perceptron neural networks for short-term load forecasting

The objective of this research is to analyze the capacity of the Multilayer Perceptron Neural Network (MLP) versus Self-Organizing Map Neural Network (SOM) for Short-Term Load Forecasting. The MLP is one of the most commonly used networks. It can be used for classification problems, model construction, series forecasting and discrete control. For the forecasting problems, a backpropagation (BP) algorithm is normally used to train the MLP Neural Network. On the other hand, the SOM is a type of artificial neural network that is trained using unsupervised data to produce a low-dimensional, discretized representation of an input space of training samples in a cell map. Historical data of real global load demand were used for the research. They were presented to the two neural networks as a daily load value, in hours, from the workdays of certain years. The load curves were obtained from the Spanish Electrical System Operator. For testing purposes, new year data were also collected. The main objective of the research is to use the capacity of MLP and SOM to classify and memorize historical data, followed by taking advantage of this memorization to identify similarities between the historical data and the demand of a new day corresponding to new year data. Certain pre-processing of the input data is needed in order to obtain good results as the demand evolves over the years. It is important to establish the measurement index used to check the accuracy of the tool. This index is the Mean Absolute Percentage Error (MAPE), which measures the accuracy of fitted time series and forecasts. Different parameter configurations for the SOM and MLP training (training periods, algorithms, etc...) are also being tested to improve the behaviour of the forecast. The results of this analysis have proved the suitability of both networks to make short-term predictions, as good results are obtained when different methodologies are applied. These tools could assist Companies, Utilities and I- - ndependent System Operators (ISO) in predicting the short-term energy demand.