%0 Journal Article
	%A Ludovic Favre and  Thibaut M. Schafer and  Jean-Luc Robyr and  Elena-Lavinia Niederhäuser
	%D 2018
	%J International Journal of Energy and Power Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 141, 2018
	%T Genetic Algorithm Optimization of the Economical, Ecological and Self-Consumption Impact of the Energy Production of a Single Building
	%U https://publications.waset.org/pdf/10009537
	%V 141
	%X This paper presents an optimization method based
on genetic algorithm for the energy management inside buildings
developed in the frame of the project Smart Living Lab (SLL)
in Fribourg (Switzerland). This algorithm optimizes the interaction
between renewable energy production, storage systems and energy
consumers. In comparison with standard algorithms, the innovative
aspect of this project is the extension of the smart regulation
over three simultaneous criteria: the energy self-consumption, the
decrease of greenhouse gas emissions and operating costs. The
genetic algorithm approach was chosen due to the large quantity
of optimization variables and the non-linearity of the optimization
function. The optimization process includes also real time data of the
building as well as weather forecast and users habits. This information
is used by a physical model of the building energy resources to predict
the future energy production and needs, to select the best energetic
strategy, to combine production or storage of energy in order to
guarantee the demand of electrical and thermal energy. The principle
of operation of the algorithm as well as typical output example of
the algorithm is presented.
	%P 640 - 644