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

ElsevierEscrivá-Escrivá, G.; Segura Heras, I.; Alcázar-Ortega, M. (2010). Application of an energy management and control system to assess the potential of different control strategies in HVAC systems. Energy and Buildings. 42(11):2258-2267. doi:10.1016/j.enbuild.2010 AbstractThe significant and continuous increment in the global electricity consumption is asking for energy saving strategies. Efficient control for heating, ventilation and air-conditioning systems (HVAC) is the most cost-effective way to minimize the use of energy in buildings. In this framework, an energy management and control system (EMCS) has been developed to schedule electricity end-uses in the campus of the Universidad Politécnica de Valencia (UPV), Spain. This paper presents an evaluation performed by using the EMCS of different control strategies for HVAC split systems. It is analyzed the effect of different schedules for a common air-conditioning device and demand response strategies are tested in several situations. The economic saving is calculated taking into account the electricity contract clauses.Finally, a test is made for the control of a group of similar devices in order to reduce the maximum peak power in consumption and to obtain a flexible load shape with the HVAC loads.The studies are then extrapolated to a larger system, the whole University campus, for which energy and economic savings are quantified.

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

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.