analyzed, demonstrating as using demand response resources is much more profitable than 22 producing this energy by other conventional technologies by using fossil fuels. 23 24

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Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarterhourly values of the Universitat Politècnica de València, a commercial customer consuming 11,500 kW.

ElsevierAlcázar Ortega, M.; Álvarez Bel, CM.; Escrivá Escrivá, G.; Domijan, A. (2012). Evaluation and assessment of demand response potential applied to the meat industry. Applied Energy. 92:84-91. doi:10.1016Energy. 92:84-91. doi:10. /j.apenergy.2011.040.-1 - EVALUATION AND ASSESSMENT OF DEMAND RESPONSE Abstract 15Demand Response has proven to be a useful mechanism that produces important benefits for 16 both the customer and the power system. In the context of an increasingly competitive electricity 17 market, where prices are constantly rising and the presence of renewable energy resources is 18 gaining prominence, this paper analyzes the flexibility potential of customers in the meat industry, 19 based on the management of the most energy consuming process in this type of segment: cooling 20 production and distribution. 21The effectiveness of the proposed actions has been successfully tested and validated in an 22 active factory that produces cured ham in Spain, where savings of about 5% in the total annual cost 23 of electricity have been assessed, together with power reductions in the range of 50% of the total 24 * Corresponding Author: Manuel Alcázar-Ortega. Institute for Energy Engineering. Universidad Politécnica de Valencia.Camino de Vera, s/n, edificio 8E, escalera F, 5ª planta. and they open the door to an innovative perspective on the evaluation of flexibility among customers 26 which are traditionally considered rigid, providing a novel approach to the management of customer 27 infrastructures in order to exploit their flexibility in electricity markets. 28 29

Elsevier Álvarez, C.; Alcázar-Ortega, M.; Escrivá-Escrivá, G.; ANTONIO GABALDON MARIN (2009) AbstractThe authors present a methodology to evaluate and quantify the economic parameters (costs and benefits) attached to customer electricity consumption by analyzing the service provided by the different "pieces" of absorbed electricity. The first step of this methodology is to perform a process oriented market segmentation to identify segments according to their flexibility potential. After that, a procedure based on comprehensive simulations to identify and quantify the actual demand that can be managed in the short term is presented and, finally, the required economic analysis is performed. The methodology, which is demonstrated with some applications to the commercial sector, not only helps the customers to integrate in flexible distribution systems but also offers the necessary economical parameters for them to integrate in electricity markets.