Neural network metamodelling in multi-objective optimization of a high latitude solar community

Abstract A solar community of 100 residential houses was optimized for Finnish conditions with the aim of achieving a 90% solar fraction for both space heating and domestic hot water. Optimization was done using a novel method based on neural network metamodelling and compared to the standard NSGA-II genetic algorithm. Compared to NSGA-II, the new method obtained a larger hypervolume by finding better solutions both in the center and edge of the non-dominated front. The combined non-dominated front of both methods was better than either one separately. The performance target was achieved as the optimal solar community designs had heating solar fractions ranging from 64% to 95%.

[1]  Andreas Witzig,et al.  Surrogate modeling for the fast optimization of energy systems , 2013 .

[2]  Gerardo Maria Mauro,et al.  Multi-objective optimization of the renewable energy mix for a building , 2016 .

[3]  H. Müller-Steinhagen,et al.  Central solar heating plants with seasonal storage in Germany , 2004 .

[4]  J. Jokisalo,et al.  Development of weighting factors for climate variables for selecting the energy reference year according to the EN ISO 15927-4 standard , 2012 .

[5]  Peter Lund.  Optimization of a community solar heating system with a heat pump and seasonal storage , 1984 .

[6]  Hamidreza Zareipour,et al.  Energy storage for mitigating the variability of renewable electricity sources: An updated review , 2010 .

[7]  Anula Khare,et al.  A review of particle swarm optimization and its applications in Solar Photovoltaic system , 2013, Appl. Soft Comput..

[8]  L. Pierpoint.  Harnessing electricity storage for systems with intermittent sources of power: Policy and R&D needs , 2016 .

[9]  Alan S. Fung,et al.  Solar community heating and cooling system with borehole thermal energy storage – Review of systems , 2016 .

[10]  Kai Sirén,et al.  Design of a Simple Control Strategy for a Community-size Solar Heating System with a Seasonal Storage , 2016 .

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Andrzej P. Wierzbicki,et al.  The Use of Reference Objectives in Multiobjective Optimization , 1979 .

[13]  Zhigang Zhou,et al.  Modelling and optimization of the smart hybrid renewable energy for communities (SHREC) , 2015 .

[14]  Luisa F. Cabeza,et al.  Thermal energy storage in building integrated thermal systems: A review. Part 1. active storage systems , 2016 .

[15]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[16]  Orhan Ekren,et al.  Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing , 2010 .

[17]  F. Haghighat,et al.  Integration of storage and renewable energy into district heating systems: A review of modelling and optimization , 2016 .

[18]  João Farinha Mendes,et al.  Optimization of a seasonal storage solar system using Genetic Algorithms , 2014 .

[19]  Hans Müller-Steinhagen,et al.  Central solar heating plants with seasonal heat storage , 2010 .

[20]  M. Hamdy,et al.  A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010 , 2013 .

[21]  Paul Denholm,et al.  Grid flexibility and storage required to achieve very high penetration of variable renewable electricity , 2011 .

[22]  Guohe Huang,et al.  Community-scale renewable energy systems planning under uncertainty—An interval chance-constrained programming approach , 2009 .

[23]  Kalyanmoy Deb,et al.  A Generative Kriging Surrogate Model for Constrained and Unconstrained Multi-objective Optimization , 2016, GECCO.

[24]  Ruzhu Wang,et al.  A review of available technologies for seasonal thermal energy storage , 2014 .

[25]  K. Sirén,et al.  Zero energy level and economic potential of small-scale building-integrated PV with different heating systems in Nordic conditions , 2016 .

[26]  Mohamed Hamdy,et al.  Mobo A New Software For Multi-objective Building Performance Optimization , 2013, Building Simulation Conference Proceedings.

[27]  Chun Chen,et al.  An interval optimization based day-ahead scheduling scheme for renewable energy management in smart distribution systems , 2015 .

[28]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[29]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[30]  T. E. Boukelia,et al.  ANN-based optimization of a parabolic trough solar thermal power plant , 2016 .

[31]  Kai Sirén,et al.  Influence of location and design on the performance of a solar district heating system equipped with borehole seasonal storage , 2015 .

[32]  Antonio Lecuona,et al.  Domestic hot water consumption vs. solar thermal energy storage: The optimum size of the storage tank , 2012 .

[33]  Bill Wong,et al.  The Performance of a High Solar Fraction Seasonal Storage District Heating System – Five Years of Operation☆ , 2012 .