Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method

In the research domain of intelligent buildings and smart home, modeling and optimization of the thermal comfort and energy consumption are important issues. This paper presents a type-2 fuzzy method based data-driven strategy for the modeling and optimization of thermal comfort words and energy consumption. First, we propose a methodology to convert the interval survey data on thermal comfort words to the interval type-2 fuzzy sets (IT2 FSs) which can reflect the inter-personal and intra-personal uncertainties contained in the intervals. This data-driven strategy includes three steps: survey data collection and pre-processing, ambiguity-preserved conversion of the survey intervals to their representative type-1 fuzzy sets (T1 FSs), IT2 FS modeling. Then, using the IT2 FS models of thermal comfort words as antecedent parts, an evolving type-2 fuzzy model is constructed to reflect the online observed energy consumption data. Finally, a multiobjective optimization model is presented to recommend a reasonable temperature range that can give comfortable feeling while reducing energy consumption. The proposed method can be used to realize comfortable but energy-saving environment in smart home or intelligent buildings.

[1]  Mohammad Hossein Fazel Zarandi,et al.  Type-2 fuzzy hybrid expert system for prediction of tardiness in scheduling of steel continuous casting process , 2012, Soft Comput..

[2]  Jerry M. Mendel,et al.  Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach , 2008, IEEE Transactions on Fuzzy Systems.

[3]  Woei Wan Tan,et al.  Analytical Structure and Characteristics of Symmetric Karnik–Mendel Type-Reduced Interval Type-2 Fuzzy PI and PD Controllers , 2012, IEEE Transactions on Fuzzy Systems.

[4]  Chia-Feng Juang,et al.  An Interval Type-2 Neural Fuzzy Chip With On-Chip Incremental Learning Ability for Time-Varying Data Sequence Prediction and System Control , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Donatien Njomo,et al.  Thermal comfort: A review paper , 2010 .

[6]  Jerry M. Mendel,et al.  Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 2, Inverse Problems , 2007, IEEE Transactions on Fuzzy Systems.

[7]  Jerry M. Mendel,et al.  Enhanced Interval Approach for encoding words into interval type-2 fuzzy sets and convergence of the word FOUs , 2010, International Conference on Fuzzy Systems.

[8]  Hazlina Selamat,et al.  Power Consumption Modeling for Indoor Environment Using Artificial Neural Network , 2013 .

[9]  Y.-Q. Zhang,et al.  Web shopping expert using new interval type-2 fuzzy reasoning , 2007, Soft Comput..

[10]  Lucian C. Coroianu,et al.  Nearest interval, triangular and trapezoidal approximation of a fuzzy number preserving ambiguity , 2012, Int. J. Approx. Reason..

[11]  Y. Censor.  Pareto optimality in multiobjective problems , 1977 .

[12]  Woei Wan Tan,et al.  Towards an efficient type-reduction method for interval type-2 fuzzy logic systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[13]  Jerry M. Mendel,et al.  On KM Algorithms for Solving Type-2 Fuzzy Set Problems , 2013, IEEE Transactions on Fuzzy Systems.

[14]  Huaguang Zhang,et al.  Mode-independent fuzzy fault-tolerant variable sampling stabilization of nonlinear networked systems with both time-varying and random delays , 2012, Fuzzy Sets Syst..

[15]  Jerry M. Mendel,et al.  Computing with words and its relationships with fuzzistics , 2007, Inf. Sci..

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

[17]  Gexiang Zhang,et al.  Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems , 2011, Soft Computing.

[18]  Jerry M. Mendel,et al.  Simplified Interval Type-2 Fuzzy Logic Systems , 2013, IEEE Transactions on Fuzzy Systems.

[19]  Stefan Chanas,et al.  On the interval approximation of a fuzzy number , 2001, Fuzzy Sets Syst..

[20]  Xiaohua Xia,et al.  A multiple objective optimisation model for building energy efficiency investment decision , 2013 .

[21]  Nor Mariah Adam,et al.  A Simple Approach for Building Cooling Load Estimation , 2005 .

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

[23]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[24]  Zhang Huaguang,et al.  Modeling, identification, and control of a class of nonlinear systems , 2001, IEEE Trans. Fuzzy Syst..

[25]  Ming Wang,et al.  Uncertainty degree and modeling of interval type-2 fuzzy sets: Definition, method and application , 2013, Comput. Math. Appl..

[26]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[27]  Xinhua Xu,et al.  A grey‐box model of next‐day building thermal load prediction for energy‐efficient control , 2008 .

[28]  Jerry M. Mendel,et al.  Type-2 Fuzzistics for Nonsymmetric Interval Type-2 Fuzzy Sets: Forward Problems , 2007, IEEE Transactions on Fuzzy Systems.

[29]  Zdenko Takác,et al.  Inclusion and subsethood measure for interval-valued fuzzy sets and for continuous type-2 fuzzy sets , 2013, Fuzzy Sets Syst..

[30]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[31]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.

[32]  Ming Wang,et al.  Uncertainty degree of interval type-2 fuzzy sets and its application to thermal comfort modelling , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[33]  Yang Fei-sheng,et al.  Stabilization of interval type-2 T-S fuzzy control systems with time varying delay , 2013, Proceedings of the 32nd Chinese Control Conference.

[34]  Dongrui Wu,et al.  On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers , 2012, IEEE Transactions on Fuzzy Systems.

[35]  Francesco Piazza,et al.  Optimization algorithms for home energy resource scheduling in presence of data uncertainty , 2013, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP).

[36]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[37]  Francesco Piazza,et al.  Optimal Home Energy Management Under Dynamic Electrical and Thermal Constraints , 2013, IEEE Transactions on Industrial Informatics.

[38]  Jerry M. Mendel,et al.  Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 1, Forward Problems , 2006, IEEE Transactions on Fuzzy Systems.

[39]  Jerry M. Mendel,et al.  Enhanced Interval Approach for Encoding Words Into Interval Type-2 Fuzzy Sets and Its Convergence Analysis , 2012, IEEE Transactions on Fuzzy Systems.

[40]  Bruno Peuportier,et al.  Using Dynamic Programming Optimization to Maintain Comfort in Building during Summer Periods , 2013 .

[41]  Huaguang Zhang,et al.  T-S Model-based Relaxed Reliable Stabilization of Networked Control Systems with Time-varying Delays under Variable Sampling , 2011 .

[42]  Kwang-Ho Won,et al.  Building Environment Analysis based on Temperature and Humidity for Smart Energy Systems , 2012, Sensors.

[43]  Ming Wang,et al.  Monotonic type-2 fuzzy neural network and its application to thermal comfort prediction , 2012, Neural Computing and Applications.

[44]  Yang Liu,et al.  Automatic calibration of a rapid flood spreading model using multiobjective optimisations , 2013, Soft Comput..