Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings

This paper concerns the development of an automatic tool, based on Fuzzy Logic, which is able to identify the proper solutions for the energy retrofitting of existing buildings. Regarding winter heating, opaque and glazing surfaces are considered in order to reduce building heat dispersions. Starting from energy diagnosis, it is possible to formulate retrofitting proposals and to evaluate the effectiveness of the intervention considering several aspects (energy savings, costs, intervention typology). The innovation of this work is represented by the application of a fuzzy logic expert system to obtain an indication about the proper interventions for building energy retrofitting, providing as inputs only few parameters, with a strong reduction in time and effort with respect to the software tools and methodologies currently applied by experts. The novelty of the paper is the easy handling properties of the developed tool, which requires only a few data about the buildings: not many such methods were developed in the last years. The energy requirements for winter heating before and after particular interventions were evaluated for a consistent set of buildings in order to produce the required knowledge base for the tool’s development. The identified appropriate inputs and outputs, their domains of discretization, the membership functions associated to each fuzzy set, and the linguistic rules were deduced on the basis of the knowledge determined in this was. Therefore, the system was successfully validated with reference to further buildings characterized by different design and architecture features, showing a good agreement with the intervention opportunities evaluated.

[1]  Anastasios I. Dounis,et al.  Artificial intelligence for energy conservation in buildings , 2010 .

[2]  V. Geros,et al.  Modeling and predicting building's energy use with artificial neural networks: Methods and results , 2006 .

[3]  Ryohei Yokoyama,et al.  Prediction of energy demands using neural network with model identification by global optimization , 2009 .

[4]  Juan J. Sendra,et al.  Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe , 2019, Applied Thermal Engineering.

[5]  Xiaopeng Li,et al.  Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller , 2020, Building and Environment.

[6]  Koen Steemers,et al.  Energy retrofit and occupant behaviour in protected housing: A case study of the Brunswick Centre in London , 2014 .

[7]  Peter P. Groumpos,et al.  Proactive Building Energy Management Methods based on Fuzzy Logic and Expert Intelligence , 2019 .

[8]  Moncef Krarti,et al.  An Overview of Artificial Intelligence-Based Methods for Building Energy Systems , 2003 .

[9]  Antonio Messineo,et al.  Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation in an office building , 2014 .

[10]  L. D. Danny Harvey,et al.  Reducing energy use in the buildings sector: measures, costs, and examples , 2009 .

[11]  J.-L. Genre,et al.  Diagnosis of the degradation state of building and cost evaluation of induced refurbishment works , 2000 .

[12]  Janusz Adamczyk,et al.  Economic and environmental benefits of thermal insulation of building external walls , 2011 .

[13]  G. T. S. Ho,et al.  A fuzzy logic approach to forecast energy consumption change in a manufacturing system , 2008, Expert Syst. Appl..

[14]  Silvia Soutullo Castro,et al.  Decision matrix methodology for retrofitting techniques of existing buildings , 2019 .

[15]  Soteris A. Kalogirou,et al.  Artificial neural networks in energy applications in buildings , 2006 .

[16]  Ruey Lung Hwang,et al.  Building envelope regulations on thermal comfort in glass facade buildings and energy-saving potenti , 2011 .

[17]  A. D'Amico,et al.  Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study , 2019 .

[18]  Paul Ruyssevelt,et al.  ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation , 2017 .

[20]  Melvin Robinson,et al.  Prediction of residential building energy consumption: A neural network approach , 2016 .

[21]  Marijana Zekic-Susac,et al.  Predicting energy cost of public buildings by artificial neural networks, CART, and random forest , 2021, Neurocomputing.

[22]  Yacine Rezgui,et al.  A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control , 2018 .

[23]  Liu Yang,et al.  Building energy efficiency in different climates , 2008 .

[24]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit strategies: A model and an application , 2012 .

[25]  P. Wargocki,et al.  Literature survey on how different factors influence human comfort in indoor environments , 2011 .

[26]  Kenichi Wada,et al.  Energy efficiency opportunities in the residential sector and their feasibility , 2012 .

[27]  Roberto Lamberts,et al.  Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review , 2020 .

[28]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[29]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[30]  John Palmer,et al.  Energy performance indoor environmental quality retrofit — a European diagnosis and decision making method for building refurbishment , 2000 .

[31]  Giacomo Chiesa,et al.  A fuzzy-logic IoT lighting and shading control system for smart buildings , 2020 .

[32]  John Psarras,et al.  Intelligent building energy management system using rule sets , 2007 .

[33]  Anne Grete Hestnes,et al.  Effective retrofitting scenarios for energy efficiency and comfort: results of the design and evaluation activities within the OFFICE project , 2002 .

[34]  John Psarras,et al.  Assessing energy-saving measures in buildings through an intelligent decision support model , 2009 .

[35]  Ali Dehghanbanadaki,et al.  Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm , 2020 .

[36]  Paola Gori,et al.  A step towards the optimization of the indoor luminous environment by genetic algorithms , 2017 .

[37]  Maria Wall,et al.  Influence of window size on the energy balance of low energy houses , 2006 .

[38]  Betul Bektas Ekici,et al.  Prediction of building energy consumption by using artificial neural networks , 2009, Adv. Eng. Softw..

[39]  Kemal Çomaklı,et al.  Optimum insulation thickness of external walls for energy saving , 2003 .

[40]  Virgilio Ciancio,et al.  Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application , 2020, Energy and Buildings.

[41]  Jean-Luc Bodnar,et al.  Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building , 2011 .

[42]  Mohamed Tabaa,et al.  Multi-objective optimization of building energy performance and indoor thermal comfort by combining artificial neural networks and metaheuristic algorithms , 2021, Energy and Buildings.

[43]  Peter P. Groumpos,et al.  Using Fuzzy Control Methods for Increasing the Energy Efficiency of Buildings , 2015, Int. J. Monit. Surveillance Technol. Res..

[44]  Stefan Lechtenböhmer,et al.  The potential for large-scale savings from insulating residential buildings in the EU , 2011 .

[45]  Gerardo Maria Mauro,et al.  Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach , 2017 .

[46]  Jun‐Ki Choi,et al.  Economic and Environmental Impacts of Community-Based Residential Building Energy Efficiency Investment , 2014 .

[47]  Lollini,et al.  Optimisation of opaque components of the building envelope. Energy, economic and environmental issues , 2006 .

[48]  Orhan Büyükalaca,et al.  A case study for influence of building thermal insulation on cooling load and air-conditioning system in the hot and humid regions , 2010 .

[49]  Paul Cooper,et al.  Existing building retrofits: Methodology and state-of-the-art , 2012 .