Genetic Algorithm Optimization of the Economical, Ecological and Self-Consumption Impact of the Energy Production of a Single Building
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.
[1] T. M. Schafer, G. Magnin, D. Vuarnoz and E.-L. Niederh¨auser,
Development and Validation of an Intelligent Algorithm for Synchronizing
a Low-Environmental-Impact Electricity Supply with a Buildings
Electricity Consumption, University of Applied Science of Fribourg and
Swiss Federal Institute of Technology of Lausanne, proceeding of the
4th International Conference on Renewable Energy Technologies, January
2018.
[2] L. J. Favre, T. M. Schafer and E.-L. Niederh¨auser, Prototype for
predictive, autonomous and innovative energy management for buildings,
University of Applied Science of Fribourg, unpublished, 2017. [3] Neurobat AG, products and technologies aimed at reducing HVAC-related
CO2 emissions and energy costs, Neurobat products NOL and NIQ:
http://neurobat.net/fr/produits/, read in December 2017.
[4] Loxone Electronics GmbH, smart home product, Miniserver:
http://shop.loxone.com/dech/miniserver.html, read in December 2017.
[5] Siemens AG, HVAC control system for small and
medium-sized multipurposed buildings, Synco product:
http://www.buildingtechnologies.siemens.com/bt, read in
December 2017. /global/en/buildingautomation-hvac/buildingautomation/
building-automation-for-small-applications-synco/pages/hvacbuilding-
automation.aspx, read in December 2017.
[6] Alpiq InTec Suisse SA, intelligent energy control, Gridsense:
https://www.gridsense.ch/en/home.html, read in December 2017.
[7] G. Magnin, M. Rouge, V. Liblin and E.-L. Niederh¨auser, Photo-PAC:
Optimization of the use of the coupling heat pumps - solar photovoltaic
thanks to the predictive management, University of Applied Science of
Fribourg, unpublished, 2015.
[8] F. Oldewurtel, A. Parisio, C.N. Jones, D. Gyalistras, M. Gwerder, V.
Stauch, B. Lehmann, M. Morari, Use of model predictive control and
weather forecasts for energy efficient building climate control, 2012,
Energy Build., 15-27, 2012.
[9] F. Oldewurtel, A. Parisio, C. Jones, M. Morari, D. Gyalistras, M. Gwerder,
V. Stauch, B. Lehmann, K.Wirth, Energy efficient building climate control
using stochastic model predictive control and weather predictions, 2010,
American Control Conference (ACC), 5100-5105, 2010.
[10] M. Killian, M. Kozek, Ten questions concerning model predictive control
for energy efficient buildings, 2016, Building and Environment vol. 105,
403-412, 2016.
[11] Binghui Si, Zhichao Tian, Xing Jin, Xin Zhou, Peng Tang, Xing Shi,
Performance indices and evaluation of algorithms in building energy
efficient design optimization, 2016, Energy vol. 114, 100-112, 2016.
[12] Sustainable development, FSO, Federal Administration:
https://www.bfs.admin.ch/bfs/en/home/statistics/sustainable-development.
html, read in December 2017.
[13] Pyevolve: A Python Open-source Framework for Genetic Algorithms.
Perone, C. 2009. 1, November 2009, SIGEVOlution, Vol. 4, pp. 12-20,
November 2009.
[14] D. Vuarnoz and T. Jusselme, Temporal varitation in the primary energy
use and greenhouse gas emissions of the electricity provided by Swiss
grid, submitted, 2017.
[15] Energy Act (EnA), Swiss Federal Council:
https://www.admin.ch/gov/en/start/documentation/votes/Popular%20vote
%20on%2021%20Mai%202017/Energy-Act.html, read in December
2017.
[16] D. Vuarnoz, T. Jusselme, S. Cozza, E. Rey and M. Andersen, Studying
the dynamic relationship between energy supply carbon content and
building energy demand,, 2016, PLEA conf. Los Angeles, 2016.
[17] H. Br¨ulhart, D. Vionnet, M. Boesiger and J.-P. Bacher, Smart Living
Lab: Laboratoire d’int´egration des ´energies renouvelables, University of
Applied Science of Fribourg, 2017., unpublished, 2017.
[18] blueFACTORY, the neighbourhood of the innovation of Fribourg:
http://www.bluefactory.ch/fr/home, read in December 2017.
[1] T. M. Schafer, G. Magnin, D. Vuarnoz and E.-L. Niederh¨auser,
Development and Validation of an Intelligent Algorithm for Synchronizing
a Low-Environmental-Impact Electricity Supply with a Buildings
Electricity Consumption, University of Applied Science of Fribourg and
Swiss Federal Institute of Technology of Lausanne, proceeding of the
4th International Conference on Renewable Energy Technologies, January
2018.
[2] L. J. Favre, T. M. Schafer and E.-L. Niederh¨auser, Prototype for
predictive, autonomous and innovative energy management for buildings,
University of Applied Science of Fribourg, unpublished, 2017. [3] Neurobat AG, products and technologies aimed at reducing HVAC-related
CO2 emissions and energy costs, Neurobat products NOL and NIQ:
http://neurobat.net/fr/produits/, read in December 2017.
[4] Loxone Electronics GmbH, smart home product, Miniserver:
http://shop.loxone.com/dech/miniserver.html, read in December 2017.
[5] Siemens AG, HVAC control system for small and
medium-sized multipurposed buildings, Synco product:
http://www.buildingtechnologies.siemens.com/bt, read in
December 2017. /global/en/buildingautomation-hvac/buildingautomation/
building-automation-for-small-applications-synco/pages/hvacbuilding-
automation.aspx, read in December 2017.
[6] Alpiq InTec Suisse SA, intelligent energy control, Gridsense:
https://www.gridsense.ch/en/home.html, read in December 2017.
[7] G. Magnin, M. Rouge, V. Liblin and E.-L. Niederh¨auser, Photo-PAC:
Optimization of the use of the coupling heat pumps - solar photovoltaic
thanks to the predictive management, University of Applied Science of
Fribourg, unpublished, 2015.
[8] F. Oldewurtel, A. Parisio, C.N. Jones, D. Gyalistras, M. Gwerder, V.
Stauch, B. Lehmann, M. Morari, Use of model predictive control and
weather forecasts for energy efficient building climate control, 2012,
Energy Build., 15-27, 2012.
[9] F. Oldewurtel, A. Parisio, C. Jones, M. Morari, D. Gyalistras, M. Gwerder,
V. Stauch, B. Lehmann, K.Wirth, Energy efficient building climate control
using stochastic model predictive control and weather predictions, 2010,
American Control Conference (ACC), 5100-5105, 2010.
[10] M. Killian, M. Kozek, Ten questions concerning model predictive control
for energy efficient buildings, 2016, Building and Environment vol. 105,
403-412, 2016.
[11] Binghui Si, Zhichao Tian, Xing Jin, Xin Zhou, Peng Tang, Xing Shi,
Performance indices and evaluation of algorithms in building energy
efficient design optimization, 2016, Energy vol. 114, 100-112, 2016.
[12] Sustainable development, FSO, Federal Administration:
https://www.bfs.admin.ch/bfs/en/home/statistics/sustainable-development.
html, read in December 2017.
[13] Pyevolve: A Python Open-source Framework for Genetic Algorithms.
Perone, C. 2009. 1, November 2009, SIGEVOlution, Vol. 4, pp. 12-20,
November 2009.
[14] D. Vuarnoz and T. Jusselme, Temporal varitation in the primary energy
use and greenhouse gas emissions of the electricity provided by Swiss
grid, submitted, 2017.
[15] Energy Act (EnA), Swiss Federal Council:
https://www.admin.ch/gov/en/start/documentation/votes/Popular%20vote
%20on%2021%20Mai%202017/Energy-Act.html, read in December
2017.
[16] D. Vuarnoz, T. Jusselme, S. Cozza, E. Rey and M. Andersen, Studying
the dynamic relationship between energy supply carbon content and
building energy demand,, 2016, PLEA conf. Los Angeles, 2016.
[17] H. Br¨ulhart, D. Vionnet, M. Boesiger and J.-P. Bacher, Smart Living
Lab: Laboratoire d’int´egration des ´energies renouvelables, University of
Applied Science of Fribourg, 2017., unpublished, 2017.
[18] blueFACTORY, the neighbourhood of the innovation of Fribourg:
http://www.bluefactory.ch/fr/home, read in December 2017.
@article{"International Journal of Electrical, Electronic and Communication Sciences:77941", author = "Ludovic Favre and Thibaut M. Schafer and Jean-Luc Robyr and Elena-Lavinia Niederhäuser", title = "Genetic Algorithm Optimization of the Economical, Ecological and Self-Consumption Impact of the Energy Production of a Single Building", abstract = "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.", keywords = "Building’s energy, control system, energy
management, modelling, genetic optimization algorithm, renewable
energy, greenhouse gases, energy storage.", volume = "12", number = "9", pages = "640-5", }
