Absrraa -Electric energy demand forecasting represents a fundamental information to plan the activities of the companies that generate and distribute it. So a good prediction of its demand will provide an invaluable tool to plan the production and purchase policies of both generation and distribution or reseller companies. This demand may be seen as a temporal series when its data are conveniently arranged. In this way the prediction of a future value may be performed stud,ying the past ones. Neural networks have proved to be a very powerful tool to do this. They are mathematical structures thalt mimic that of the nervous system of living beings and are used extensively for system identification and prediction of their future evolution. In this work a neural network is presented1 to predict the evolution of the monthly demand of electric consumption. A Feedforward Multilayer Perceptron (MLP) has been used as neural model with Backpropagation as learning strategy. The network has three hidden layers with a 8-4-8 distribution. It takes twelve past values to predict the following one. Errors smaller than 5% have been obtained in most of the predictions.

Abstract.Achieving Nearly Zero-Energy Buildings (NZEBs) is a main goal for the European Union, in order to reduce energy consumption in the building sector. NZEB means a building that has a very high energy performance. Its energy requirements should be covered by renewable sources, produced on-site or nearby [1]. It could be possible if building were turned into a "small power generating station", or reducing consumption with passive building proposals. However, we think that it is worth looking for a balance between energy consumption and generation for every building, following this simple equation: Consumption = demand -generationThe European regulations have already begun to indicate deadlines to implement NZEB requirements in buildings. This study was performed by using a computer building model, including its geometry, building materials, usage profiles and installations. Thus, we could compare the characteristics of the different regulations, and we could evaluate the progress toward the NZEB concept.

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Abstract. Electric energy demand forecasting represents a fundamental tool to plan the activities of the companies that generate and distribute it. So a good prediction of its demand will provide an invaluable information to plan the production and purchase policies of these companies. This demand may be seen as a temporal series when these data are conveniently arranged. In this way the prediction of a future value may be performed studying the past ones. Neural networks have proved to be a very powerful tool to do this. They are mathematical structures that mimic that of the nervous system of living beings and are used extensively for system identification and prediction of their future evolution. In this work a neural network is presented to predict the evolution of the monthly demand of electric consumption. A Feedforward Multilayer Perceptron (MLP) with three hidden layers has been used as neural model with Backpropagation as learning strategy. The consumption data have been normalized to avoid their rising trend. Several procedures have been tested in order to find out those performing the best. Errors smaller than 5% have been obtained in most of the predictions.