![]() Pohekar, S.D., Ramachandran, M.: Application of multi-criteria decision making to sustainable energy planning-A review. Jiangjiang, W., Zhiqiang, J.Z., Youyin, J., Chunfa, Z.: Particle swarm optimization for redundant building cooling heating and power system. Energy and Buildings (2010)Ĭhwieduk, D.: Towards sustainable-energy buildings. Morosan, P.D., Bourdais, R., Dumur, D., Buisson, J.: Building temperature regulation using a distributed model predictive control. Pérez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Yang, I.H., Yeo, M.S., Kim, K.W.: Application of artificial neural network to predict the optimal start time for heating system in building. ![]() Multi Attribute Utility Theory (MAUT) is used for this. Since thermal comfort is a subjective multidimensional concept, an interpretable and reusable preference model is introduced in this paper. However, they are not easily interpretable in terms of a preference model which makes control not intuitive and tractable. Literature proposes reusable system independent statistical models for thermal comfort. ![]() Therefore, the DSS aims to compute the most relevant target values (i.e., setpoints) to be provided to the energy control system and then, improving thermal comfort sensation or reducing energy costs. Particularly, our RIDER Decision Support System (DSS) focuses on proposing generic control rules and optimization techniques for energy management systems. In this context, RIDER project tries to develop a weak system dependency of energy management framework which could be applied for different systems. ![]() In response to this situation, many projects have been started in order to save energy. The incessant need for energy has raised its cost to unexpected heights. ![]()
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