A thermodynamical model study for an energy saving algorithm Enrique de la Cal 1 , José Ramón Villar 1 , Javier Sedano 2 1 University of Oviedo, Computer Science department, Edificio Departamental 1, Campus de Viesques s/n, 33204 Gijón, Spain {villarjose, delacal}@uniovi.es 2 University of Burgos, Electromechanic department, Escuela Politécnica Superior, Campus Vena, B2 09006 Burgos, Spain jsedano@ubu.es Abstract. A local Spanish company that produces electric heaters needs an energy saving device to be integrated with the heaters. It was proven that a hybrid artificial intelligent systems (HAIS) could afford the energy saving reasonably, even though some improvements must be introduced. One of the critical elements in the process of designing an energy saving system is the thermodynamical modelling of the house to be controlled. This work presents a study of different first order techniques, some taken from the literature and other new proposals, for the prediction of the thermal dynamics in a house. Finally it is concluded that a first order prediction system is not a valid prediction model for such an energy saving system. Keywords: Fuzzy systems, Hybrid Artificial Intelligence Systems, Real World Applications, Thermodynamical Modelling, Electric Energy saving. 1 Motivation In Spain, a local company has developed a new dry electric heaters production line and a device for electric energy saving (EES) is to be included in the catalogue. In previous works, the development of such a device has been analyzed, and a multi agent hybrid fuzzy system has been proposed [12] [13] [11]. There are two stages in defining the proposed energy distribution algorithm that goes in the CCU: the design stage and the run stage. In the design stage a wide range fuzzy controller is trained and validated out of the hardware device, while in the run stage the whole algorithm is executed in the embedded hardware (CCU) The first preliminary EES was proposed in [12]. In that proposal, a thermodynamical model (from now on TM) of the house to be controlled was not required, because the fuzzy energy distribution controller (FC) was defined directly by a team of experts. In our next work, [13], a new system design was presented (see Figure 1) and two improvements were introduced: an optimization step to improve the Experts FC