Type-2 fuzzy wavelet neural network for estimation energy performance of residential buildings

The estimation of the energy performance of residential buildings has gained importance because of the significant consumption of electricity in housing estate areas. For this aim, different approaches were utilized for robust and accurate prediction of the energy load in buildings. The use of different kind of construction materials, timely change in building parameters lead to imprecise and vague evaluation of energy consumption. For such kind of problems that are characterized with uncertainties, the use of fuzzy set theory is a more suitable approach for the modeling of energy consumption. This paper proposes a novel type-2 fuzzy wavelet neural network (T2FWNN) for modeling the energy performance of residential buildings. Based on the type-2 fuzzy rules, the multi-input multi-output T2FWNN model is proposed. For the construction of the T2FWNN model, the learning algorithm has been designed using cross-validation approach, clustering and gradient descent algorithms. During construction, the adaptive learning procedure was developed to stabilize and speed up the learning process. The proposed model is used for the solution of two problems. At the first stage, based on statistical data, the T2FWNN model has been designed for modeling the cooling and heating load of residential buildings. In the second stage, using T2FWNN the prediction model was designed for the energy consumption of residential buildings in Northern Cyprus. Comparative results have been provided to prove the efficiency of using the designed model in the prediction of the energy load of residential buildings. The obtained results indicated the suitability of using the T2FWNN system for estimation of the energy performance and prediction of the energy consumption of residential buildings.

[1]  Wei-Chiang Hong,et al.  Forecasting electricity consumption using a novel hybrid model , 2020, Sustainable Cities and Society.

[2]  Bing Dong,et al.  A hybrid model approach for forecasting future residential electricity consumption , 2016 .

[3]  Jerry M. Mendel,et al.  Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters , 2000, IEEE Trans. Fuzzy Syst..

[4]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[5]  Martin Vilela,et al.  A fuzzy inference system applied to value of information assessment for oil and gas industry , 2019, Decision Making: Applications in Management and Engineering.

[6]  Qionghua Wang,et al.  Application of ALD-Al 2 O 3 in CdS/CdTe Thin-Film Solar Cells , 2019 .

[7]  Chirag Deb,et al.  Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks , 2016 .

[8]  Rahib H. Abiyev,et al.  Credit Rating Using Type-2 Fuzzy Neural Networks , 2014 .

[9]  Rahib Hidayat Abiyev,et al.  A Type-2 Fuzzy Wavelet Neural Network for Time Series Prediction , 2010, IEA/AIE.

[10]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[11]  Adel M. Alimi,et al.  A Beta basis function Interval Type-2 Fuzzy Neural Network for time series applications , 2018, Eng. Appl. Artif. Intell..

[12]  Wei-Chiang Hong,et al.  Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting , 2019, Energies.

[13]  Rajesh Kumar,et al.  Energy analysis of a building using artificial neural network: A review , 2013 .

[14]  Georgios Kokogiannakis,et al.  History and development of validation with the ESP-r simulation program , 2008 .

[15]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[16]  Maryam Zekri,et al.  Adaptive fuzzy wavelet network control design for nonlinear systems , 2008, Fuzzy Sets Syst..

[17]  Sholahudin,et al.  Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments , 2016 .

[18]  Hani Hagras,et al.  A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots , 2004, IEEE Transactions on Fuzzy Systems.

[19]  Wei-Chiang Hong,et al.  A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back propagation neural network for mid‐short term load forecasting , 2020 .

[20]  F. Haghighat,et al.  Development of Artificial Neural Network based heat convection algorithm for thermal simulation of large rectangular cross-sectional area Earth-to-Air Heat Exchangers , 2010 .

[21]  Youngdeok Hwang,et al.  Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings , 2016 .

[22]  Christian Esposito,et al.  Blockchain-based authentication and authorization for smart city applications , 2021, Inf. Process. Manag..

[23]  Chin-Teng Lin,et al.  An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[24]  J. Mendel,et al.  Parametric design of stable type-2 TSK fuzzy systems , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[25]  Herzegovina,et al.  ANFIS model for the prediction of generated electricity of photovoltaic modules , 2019 .

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

[27]  Rahib Hidayat Abiyev.  Fuzzy wavelet neural network for prediction of electricity consumption , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[28]  Wei-Chiang Hong,et al.  Electric load forecasting by support vector model , 2009 .

[29]  Jerry M. Mendel,et al.  On the Stability of Interval Type-2 TSK Fuzzy Logic Control Systems , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Jun Zhang,et al.  Wavelet neural networks for function learning , 1995, IEEE Trans. Signal Process..

[31]  Da Yan,et al.  DeST — An integrated building simulation toolkit Part I: Fundamentals , 2008 .

[32]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[33]  Oscar Castillo,et al.  Type-2 Fuzzy Logic: Theory and Applications , 2007, IEEE International Conference on Granular Computing.

[34]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[35]  Hossein Moayedi,et al.  Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms , 2019, Appl. Soft Comput..

[36]  Jerry M. Mendel,et al.  Applications of Type-2 Fuzzy Logic Systems to Forecasting of Time-series , 1999, Inf. Sci..

[37]  Daniel W. C. Ho,et al.  Fuzzy wavelet networks for function learning , 2001, IEEE Trans. Fuzzy Syst..

[38]  Okyay Kaynak,et al.  A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization , 2011, Appl. Soft Comput..

[39]  R. Sullivan,et al.  Validation studies of the DOE-2 Building Energy Simulation Program. Final Report , 1998 .

[40]  Nelson Fumo,et al.  A review on the basics of building energy estimation , 2014 .

[41]  Zichen Zhang,et al.  Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm , 2019, Nonlinear Dynamics.

[42]  Okyay Kaynak,et al.  A servo system control with time-varying and nonlinear load conditions using type-2 TSK fuzzy neural system , 2011, Appl. Soft Comput..

[43]  Athanasios Tsanas,et al.  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .

[44]  Sanan Abizada,et al.  Energy Consumption Prediction of Residential Buildings Using Fuzzy Neural Networks , 2018 .

[45]  Brij B. Gupta,et al.  An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols , 2018, Concurr. Comput. Pract. Exp..

[46]  Okyay Kaynak,et al.  Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants , 2010, IEEE Transactions on Industrial Electronics.

[47]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

[48]  Y. Wang,et al.  A Type-2 Fuzzy Switching Control System for Biped Robots , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[49]  Okyay Kaynak,et al.  A type-2 fuzzy wavelet neural network for system identification and control , 2013, J. Frankl. Inst..

[50]  Pranab K. Muhuri,et al.  Big-data clustering with interval type-2 fuzzy uncertainty modeling in gene expression datasets , 2019, Eng. Appl. Artif. Intell..

[51]  Fan Zhang,et al.  Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique , 2016 .

[52]  Haoxiang Wang,et al.  Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings , 2017, Future Gener. Comput. Syst..

[53]  M. Goyal,et al.  A novel framework for risk assessment and resilience of critical infrastructure towards climate change , 2021 .