臺灣博碩士論文加值系統

The building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. Improving building energy efficiency is imperative for reducing energy costs and the environmental impact. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning model for predicting building energy consumption. Real-time data retrieved from a smart grid installed in an experimental building were used to evaluate the efficacy and effectiveness of the proposed model. The proposed model integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed novel time-series forecasting model fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. The MetaFA is proposed to optimize prediction accuracy of the proposed model. A k-week sliding window approach is used for employing historical data as inputs for the novel time-series forecasting model. The prediction model yielded high and reliable accuracy in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kWh. Notably, the model demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially during peak times.

The building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. Improving building energy efficiency is imperative for reducing energy costs and the environmental impact. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning model for predicting building energy consumption. Real-time data retrieved from a smart grid installed in an experimental building were used to evaluate the efficacy and effectiveness of the proposed model. The proposed model integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed novel time-series forecasting model fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. The MetaFA is proposed to optimize prediction accuracy of the proposed model. A k-week sliding window approach is used for employing historical data as inputs for the novel time-series forecasting model. The prediction model yielded high and reliable accuracy in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kWh. Notably, the model demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially during peak times.

ABSTRACT i
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF FIGURES viii
LIST OF TABLES viii
NOMENCLATURE viii
CHAPTER 1: INTRODUCTION 8
1.1 Research Background and Motivations 8
1.2 Research Objectives 8
1.3 Research Contributions 8
1.4 Research Scope 8
1.5 Dissertation Organization 8
CHAPTER 2: LITERATURE REVIEW 8
2.1 Building Energy Management 8
2.2 Time-Series Modeling for Building Energy Consumption 8
2.3 Machine Learning for Predicting Building Energy Consumption 8
2.4 Swarm Intelligence Algorithms 8
2.5 Hybrid Approach-based Prediction Methods 8
2.6 Chapter Summary 8
CHAPTER 3: RESEARCH METHODOLOGY 8
3.1 Overview of Research Methodology 8
3.2 Time-Series Metaheuristic Optimization-Based Machine Learning Model 8
3.2.1 Time-Series Modeling and Forecasting 8
3.2.2. Machine Learning 8
3.2.3 Nature-Inspired Metaheuristic Optimization Algorithm 8
3.2.4 Performance Measures for Prediction Model 8
3.3. Model Development Environment 8
3.3.1 Programming Languages 8
3.3.2 Database Management Tools 8
3.3.3 Task Scheduler 8
CHAPTER 4: SMART GRID EXPERIMENT AND MODEL DEVELOPMENT 8
4.1 Residential Building for Smart Grid Experiment 8
4.2 Smart Grid Metering Infrastructure 8
4.2.1 Metering and Sub-metering Infrastructure 8
4.2.2 Communication Network 8
4.2.3 Data management Infrastructure 8
4.3 Data Collection and Preprocessiing 8
4.4 Prediction Model Development 8
CHAPTER 5: MODEL EVALUATION AND APPLICATION 8
5.1 Prediction Model Settings 8
5.2 Sensitivity Analysis 8
5.3 Evaluation Results and Discussion 8
5.4 Model Application 8
5.4.1. Model Compilation 8
5.4.2. Application Demostration 8
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 8
6.1 Concluding Remarks 8
6.2 Research Contributions 8
6.3 Future Works 8
REFERENCES 8
APPENDIX 8
Appendix A. Used Hardware and Software for Model Development 8
Appendix B. MATLAB Codes of MetaFA for Solving Benchmark Functions 8
Appendix C. An Example of 1-Week Historical Data in “input” Table 8
Appendix D. Results of Sensitivity Analysis 8
D.1. Performance Measures of Sensitive Analysis 1 for Test Data 8
D.2. Performance Measures of Sensitive Analysis 2 for Test Data 8
Appendix E. Actual and Predicted Energy Consumption for Seven Evaluations 8
Appendix F. MySQL Procedure Codes for Converting 1-minute Data to 15-minute Data 8
F.1 Input_Insert_Procedure_Monday_July20 8
F.2 Input_Insert_Procedure_Tuesday_July21 8
F.3 Input_Insert_Procedure_Wednesday_July22 8
F.4 Input_Insert_Procedure_Thursday_July23 8
F.5 Input_Insert_Procedure_Friday_July24 8
F.6 Input_Insert_Procedure_Saturday_July25 8
F.7 Input_Insert_Procedure_Sunday_July26 8
Appendix G. MABLAB Codes of SARIMA-MetaFA-LSSVR Model 8
G.1 MABLAD Codes of SARIMA-MetaFA-LSSVR model for Evaluation 8
G.2 MATLAB Codes of SARIMA-MetaFA-LSSVR Model for Forecasting 8
G.3 Procedure in SARIMA-MetaFA-LSSVR Model 8
Appendix H. C# Codes in Visual Studio for Compiling Automated Prediction System 8

[1]https://downloads.mysql.com/archives/administrator/, Vol. 2016, 2016.
[2]https://en.wikipedia.org/wiki/XAMPP, (2016).
[3]C. Aghemo, L. Blaso, A. Pellegrino, Building automation and control systems: a case study to evaluate the energy and environmental performances of a lighting control system in offices, Automation in Construction 43 (2014) 10-22.
[4]A.S. Ahmad, M.Y. Hassan, M.P. Abdullah, H.A. Rahman, F. Hussin, H. Abdullah, R. Saidur, A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renewable and Sustainable Energy Reviews 33 (2014) 102-109.
[5]A. Allouhi, Y. El Fouih, T. Kousksou, A. Jamil, Y. Zeraouli, Y. Mourad, Energy consumption and efficiency in buildings: current status and future trends, Journal of Cleaner Production 109 (2015) 118-130.
[6]A. Altun, M. Şahman, Cost optimization of mixed feeds with the particle swarm optimization method, Neural Computing and Applications 22 (2) (2013) 383-390.
[7]N. Arghira, L. Hawarah, S. Ploix, M. Jacomino, Prediction of appliances energy use in smart homes, Energy 48 (1) (2012) 128-134.
[8]T. Bapat, N. Sengupta, S.K. Ghai, V. Arya, Y.B. Shrinivasan, D. Seetharam, User-sensitive scheduling of home appliances, Proceedings of the 2nd ACM SIGCOMM Workshop on Green Networking, ACM, Toronto, Ontario, Canada, 2011, pp. 43-48.
[9]G.E.P. Box, G. Jenkins, Time series analysis, forecasting and control, Holden-Day, Incorporated, California, United States, 1990.
[10]G.E.P. Box, G.M. Jenkins, Time series analysis: forecasting and control. 3rd ed., Holden-day, California, United States, 1970.
[11]M.S. Cao, L.X. Pan, Y.F. Gao, D. Novák, Z.C. Ding, D. Lehký, X.L. Li, Neural network ensemble-based parameter sensitivity analysis in civil engineering systems, Neural Computing and Applications (2015) 1-8.
[12]J. Che, J. Wang, Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling, Energy Conversion and Management 51 (10) (2010) 1911-1917.
[13]H. Chen, Y. Huang, H. Shen, Y. Chen, M. Ru, Y. Chen, N. Lin, S. Su, S. Zhuo, Q. Zhong, X. Wang, J. Liu, B. Li, S. Tao, Modeling temporal variations in global residential energy consumption and pollutant emissions, Applied Energy (2015).
[14]J. Chen, J. Yang, J. Zhao, F. Xu, Z. Shen, L. Zhang, Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method, Neurocomputing 174 (2015) 1087-1100.
[15]K.-Y. Chen, C.-H. Wang, A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan, Expert Systems with Applications 32 (1) (2007) 254-264.
[16]S.-Y. Chen, Y.-S. Lu, C.-F. Lai, A smart appliance management system with current clustering algorithm in home network, in: J.P.C. Rodrigues, L. Zhou, M. Chen, A. Kailas (Eds.), Green Communications and Networking, Vol. 51, Springer Berlin Heidelberg, 2012, pp. 13-24.
[17]T.-M. Choi, Y. Yu, K.-F. Au, A hybrid SARIMA wavelet transform method for sales forecasting, Decision Support Systems 51 (1) (2011) 130-140.
[18]J.-S. Chou, M.-Y. Cheng, Y.-W. Wu, Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models, Expert Systems with Applications 40 (6) (2013) 2263-2274.
[19]J.-S. Chou, C. Lin, Predicting Disputes in Public-Private Partnership Projects: Classification and Ensemble Models, Journal of Computing in Civil Engineering 27 (1) (2013) 51-60.
[20]J.-S. Chou, N.-T. Ngo, A.-D. Pham, Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression, Journal of Computing in Civil Engineering 0 (0) (2015) 04015002.
[21]J.-S. Chou, N.-T. Ngo, A.-D. Pham, Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression, Journal of Computing in Civil Engineering 30 (1) (2016) 04015002.
[22]J.-S. Chou, A.-D. Pham, Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials 49 (2013) 554-563.
[23]J.-S. Chou, A.S. Telaga, Real-time detection of anomalous power consumption, Renewable and Sustainable Energy Reviews 33 (2014) 400-411.
[24]L.d.S. Coelho, V.C. Mariani, Improved firefly algorithm approach applied to chiller loading for energy conservation, Energy and Buildings 59 (2013) 273-278.
[25]I. Colak, G. Fulli, S. Sagiroglu, M. Yesilbudak, C.-F. Covrig, Smart grid projects in Europe: current status, maturity and future scenarios, Applied Energy 152 (2015) 58-70.
[26]E. Corry, P. Pauwels, S. Hu, M. Keane, J. O'Donnell, A performance assessment ontology for the environmental and energy management of buildings, Automation in Construction 57 (2015) 249-259.
[27]A. Costa, M.M. Keane, P. Raftery, J. O’Donnell, Key factors methodology - a novel support to the decision making process of the building energy manager in defining optimal operation strategies, Energy and Buildings 49 (2012) 158-163.
[28]M. Črepinšek, S.-H. Liu, M. Mernik, Exploration and exploitation in evolutionary algorithms: A survey, ACM Comput. Surv. 45 (3) (2013) 1-33.
[29]E. Crisostomi, C. Gallicchio, A. Micheli, M. Raugi, M. Tucci, Prediction of the Italian electricity price for smart grid applications, Neurocomputing 170 (2015) 286-295.
[30]B. Cui, D.-c. Gao, S. Wang, X. Xue, Effectiveness and life-cycle cost-benefit analysis of active cold storages for building demand management for smart grid applications, Applied Energy 147 (2015) 523-535.
[31]E. D'Andrea, B. Lazzerini, S.L. del Rosario, Neural network-based forecasting of energy consumption due to electric lighting in office buildings, Sustainable Internet and ICT for Sustainability, 2012, pp. 1-5.
[32]Z. Da-yong, S. Hong-wei, C. Pu, Stock market forecasting model based on a hybrid ARMA and support vector machines, 15th Annual Conference Proceedings., International Conference on Management Science and Engineering, 2008, pp. 1312-1317.
[33]A.C. Davies, An overview of bluetooth wireless technology and some competing LAN standards, 1st IEEE International Conference on Circuits and Systems for Communications, 2002, pp. 206-211.
[34]L. De Boeck, S. Verbeke, A. Audenaert, L. De Mesmaeker, Improving the energy performance of residential buildings: A literature review, Renewable and Sustainable Energy Reviews 52 (2015) 960-975.
[35]S.S.S.R. Depuru, L. Wang, V. Devabhaktuni, Smart meters for power grid: challenges, issues, advantages and status, Renewable and Sustainable Energy Reviews 15 (6) (2011) 2736-2742.
[36]K.G. Di Santo, E. Kanashiro, S.G. Di Santo, M.A. Saidel, A review on smart grids and experiences in Brazil, Renewable and Sustainable Energy Reviews 52 (2015) 1072-1082.
[37]R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, IEEE, Nagoya, 1995, pp. 39-43.
[38]B.B. Ekici, U.T. Aksoy, Prediction of building energy consumption by using artificial neural networks, Advances in Engineering Software 40 (5) (2009) 356-362.
[39]M.E. El-hawary, The smart grid—state-of-the-art and future trends, Electric Power Components and Systems 42 (3-4) (2014) 239-250.
[40]D.o. Energy, Buildings Energy Data Book, Department of Energy, 2009.
[41]U.S.D.o. Energy, Grid 2030 - A National Vision for Electricity`s Second 100 years, United States Department of Energy, 2003.
[42]C. Fan, F. Xiao, S. Wang, Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques, Applied Energy 127 (2014) 1-10.
[43]P. Faria, Z. Vale, Demand response in electrical energy supply: an optimal real time pricing approach, Energy 36 (8) (2011) 5374-5384.
[44]S. Firth, K. Lomas, A. Wright, R. Wall, Identifying trends in the use of domestic appliances from household electricity consumption measurements, Energy and Buildings 40 (5) (2008) 926-936.
[45]I. Fister, I. Fister Jr, X.-S. Yang, J. Brest, A comprehensive review of firefly algorithms, Swarm and Evolutionary Computation 13 (2013) 34-46.
[46]I. Fister Jr, M. Perc, S.M. Kamal, I. Fister, A review of chaos-based firefly algorithms: Perspectives and research challenges, Applied Mathematics and Computation 252 (2015) 155-165.
[47]A. Foucquier, S. Robert, F. Suard, L. Stéphan, A. Jay, State of the art in building modelling and energy performances prediction: A review, Renewable and Sustainable Energy Reviews 23 (2013) 272-288.
[48]M. Friedman, A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings, The Annals of Mathematical Statistics 11 (1) (1940) 86-92.
[49]M. Friedman, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Journal of the American Statistical Association 32 (200) (1937) 675-701.
[50]C.A. Fróes Lima, J.R. Portillo Navas, Smart metering and systems to support a conscious use of water and electricity, Energy 45 (1) (2012) 528-540.
[51]N. Fumo, A review on the basics of building energy estimation, Renewable and Sustainable Energy Reviews 31 (2014) 53-60.
[52]N. Fumo, M.A. Rafe Biswas, Regression analysis for prediction of residential energy consumption, Renewable and Sustainable Energy Reviews 47 (2015) 332-343.
[53]A.H. Gandomi, X.S. Yang, S. Talatahari, A.H. Alavi, Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation 18 (1) (2013) 89-98.
[54]D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989.
[55]J.J. Guo, J.Y. Wu, R.Z. Wang, A new approach to energy consumption prediction of domestic heat pump water heater based on grey system theory, Energy and Buildings 43 (6) (2011) 1273-1279.
[56]W. Haifeng, H. Dejin, Comparison of SVM and LS-SVM for Regression, International Conference on Neural Networks and Brain, 2005, Vol. 1, 2005, pp. 279-283.
[57]H. Haklı, H. Uğuz, A novel particle swarm optimization algorithm with Levy flight, Applied Soft Computing 23 (2014) 333-345.
[58]C. Hamzacebi, H.A. Es, Forecasting the annual electricity consumption of Turkey using an optimized grey model, Energy 70 (2014) 165-171.
[59]J.V. Hansen, R.D. Nelson, Time-series analysis with neural networks and ARIMA-neural network hybrids, Journal of Experimental & Theoretical Artificial Intelligence 15 (3) (2003) 315-330.
[60]Z. Haydari, F. Kavehnia, M. Askari, M. Ganbariyan, Time-series load modelling and load forecasting using neuro-fuzzy techniques, 9th International Conference on Electrical Power Quality and Utilisation 2007, pp. 1-6.
[61]D. He, C. He, L.G. Jiang, H.W. Zhu, G.R. Hu, Chaotic characteristics of a one-dimensional iterative map with infinite collapses, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 48 (7) (2001) 900-906.
[62]W.-C. Hong, Y. Dong, L.-Y. Chen, S.-Y. Wei, SVR with hybrid chaotic genetic algorithms for tourism demand forecasting, Applied Soft Computing 11 (2) (2011) 1881-1890.
[63]D. Hsu, Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data, Applied Energy 160 (2015) 153-163.
[64]R.J. Hyndman, G. Athanasopoulos, Forecasting: principles and practice, Otexts, https://www.otexts.org/fpp, 2013.
[65]IEC, IEC smart grid standardizatin roadmap eddtion 1.0, SMB Smart Grid Strategic Group, 2010.
[66]R.L. Iman, J.M. Davenport, Approximations of the critical region of the fbietkan statistic, Communications in Statistics - Theory and Methods 9 (6) (1980) 571-595.
[67]M. Jamil, X.-S. Yang, A Literature Survey of Benchmark Functions For Global Optimization Problems, International Journal of Mathematical Modelling and Numerical Optimisation 4 (2) (2013) 150-194.
[68]H.C. Jung, J.S. Kim, H. Heo, Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach, Energy and Buildings 90 (2015) 76-84.
[69]D.J. Kang, J.J. Lee, B.H. Kim, D. Hur, Proposal strategies of key management for data encryption in SCADA network of electric power systems, International Journal of Electrical Power & Energy Systems 33 (9) (2011) 1521-1526.
[70]D. Karaboga, B. Akay, A comparative study of Artificial Bee Colony algorithm, Applied Mathematics and Computation 214 (1) (2009) 108-132.
[71]D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (3) (2007) 459-471.
[72]A. Kazem, E. Sharifi, F.K. Hussain, M. Saberi, O.K. Hussain, Support vector regression with chaos-based firefly algorithm for stock market price forecasting, Applied Soft Computing 13 (2) (2013) 947-958.
[73]I. Khandelwal, R. Adhikari, G. Verma, Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition, Procedia Computer Science 48 (2015) 173-179.
[74]M. Khashei, M. Bijari, A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Applied Soft Computing 11 (2) (2011) 2664-2675.
[75]L. Klein, J.-Y. Kwak, G. Kavulya, F. Jazizadeh, B. Becerik-Gerber, P. Varakantham, M. Tambe, Coordinating occupant behavior for building energy and comfort management using multi-agent systems, Automation in Construction 22 (2012) 525-536.
[76]D. Kolokotsa, The role of smart grids in the building sector, Energy and Buildings 116 (2016) 703-708.
[77]J.O. L. Perez-Lombarda, C. Pout, A review on buildings energy consumption information, Energy & Buildings, 40 (2008) 394–398.
[78]C. Lach, A. Punchihewa, Smart home system operating remotely Via 802.11b/g wireless technology, Proceedings of the Fourth International Conference Computational Intelligence and Robotics and Autonomous Systems, 2007.
[79]H.I. Lee, Energy report - lower the price of electricity, Bureau of Energy, Ministry of Economic Affairs, Taiwan, 2011, pp. 35-38.
[80]Y.-S. Lee, L.-I. Tong, Forecasting nonlinear time series of energy consumption using a hybrid dynamic model, Applied Energy 94 (2012) 251-256.
[81]Y.M. Lee, L. An, F. Liu, R. Horesh, Y.T. Chae, R. Zhang, Applying science and mathematics to big data for smarter buildings, Annals of the New York Academy of Sciences 1295 (1) (2013) 18-25.
[82]K. Li, C. Hu, G. Liu, W. Xue, Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis, Energy and Buildings 108 (2015) 106–113.
[83]Q. Li, Q. Meng, J. Cai, H. Yoshino, A. Mochida, Applying support vector machine to predict hourly cooling load in the building, Applied Energy 86 (10) (2009) 2249-2256.
[84]X. Lü, T. Lu, C.J. Kibert, M. Viljanen, Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach, Applied Energy 144 (2015) 261-275.
[85]F.H. Magnago, J. Alemany, J. Lin, Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market, International Journal of Electrical Power & Energy Systems 68 (2015) 142-149.
[86]A. Mahmood, N. Javaid, S. Razzaq, A review of wireless communications for smart grid, Renewable and Sustainable Energy Reviews 41 (2015) 248-260.
[87]F.F. Martins, A. Begonha, M. Amália Sequeira Braga, Prediction of the mechanical behavior of the Oporto granite using Data Mining techniques, Expert Systems with Applications 39 (10) (2012) 8778-8783.
[88]R. Mena, F. Rodríguez, M. Castilla, M.R. Arahal, A prediction model based on neural networks for the energy consumption of a bioclimatic building, Energy and Buildings 82 (2014) 142-155.
[89]A. Mousavi, V. Vyatkin, Energy efficient agent function block: a semantic agent approach to IEC 61499 function blocks in energy efficient building automation systems, Automation in Construction 54 (2015) 127-142.
[90]S. Naji, S. Shamshirband, H. Basser, A. Keivani, U.J. Alengaram, M.Z. Jumaat, D. Petković, Application of adaptive neuro-fuzzy methodology for estimating building energy consumption, Renewable and Sustainable Energy Reviews 53 (2016) 1520-1528.
[91]A. Nickabadi, M.M. Ebadzadeh, R. Safabakhsh, A novel particle swarm optimization algorithm with adaptive inertia weight, Applied Soft Computing 11 (4) (2011) 3658-3670.
[92]P.-F. Pai, C.-S. Lin, A hybrid ARIMA and support vector machines model in stock price forecasting, Omega 33 (6) (2005) 497-505.
[93]I. Pavlyukevich, Lévy flights, non-local search and simulated annealing, Journal of Computational Physics 226 (2) (2007) 1830-1844.
[94]F. Radulovic, M. Poveda-Villalón, D. Vila-Suero, V. Rodríguez-Doncel, R. García-Castro, A. Gómez-Pérez, Guidelines for linked data generation and publication: an example in building energy consumption, Automation in Construction 57 (2015) 178-187.
[95]C. Reinisch, M. Kofler, F. Iglesias, W. Kastner, Think home energy efficiency in future smart homes, EURASIP Journal on Embedded Systems 2011 (1) (2011) 104617.
[96]C.M.C. Roque, P.A.L.S. Martins, Differential evolution for optimization of functionally graded beams, Composite Structures 133 (2015) 1191-1197.
[97]R. Sallehuddin, S.M. Hj. Shamsuddin, Hybrid grey relationnal artificial neural network and auto regressive integrated moving average model for forecasting time-series data, Applied Artificial Intelligence 23 (5) (2009) 443-486.
[98]F. Sánchez Lasheras, F.J. de Cos Juez, A. Suárez Sánchez, A. Krzemień, P. Riesgo Fernández, Forecasting the COMEX copper spot price by means of neural networks and ARIMA models, Resources Policy 45 (2015) 37-43.
[99]C. Sandels, J. Widén, L. Nordström, E. Andersson, Day-ahead predictions of electricity consumption in a Swedish office building from weather, occupancy, and temporal data, Energy and Buildings 108 (2015) 279–290.
[100]Y.D. Sergeyev, D.E. Kvasov, A deterministic global optimization using smooth diagonal auxiliary functions, Communications in Nonlinear Science and Numerical Simulation 21 (1–3) (2015) 99-111.
[101]S. Shamshirband, K. Mohammadi, P.L. Yee, D. Petković, A. Mostafaeipour, A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation, Renewable and Sustainable Energy Reviews 52 (2015) 1031-1042.
[102]Y. Shi, R. Eberhart, A modified particle swarm optimizer, The IEEE International Conference on Evolutionary Computation, 1998, , 1998, pp. 69-73.
[103]J. Soares, M.A. Fotouhi Ghazvini, Z. Vale, P.B. de Moura Oliveira, A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads, Applied Energy 162 (2016) 1074-1088.
[104]T.H. Solomon, E.R. Weeks, H.L. Swinney, Chaotic advection in a two-dimensional flow: Lévy flights and anomalous diffusion, Physica D: Nonlinear Phenomena 76 (1–3) (1994) 70-84.
[105]Standards Australia Committee BD-002, Concrete structures, SAI Global Limited, Standards Australia, GPO Box 476, Sydney, NSW 2001, Australia, 2009.
[106]R. Storn, K. Price, Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, J. of Global Optimization 11 (4) (1997) 341-359.
[107]S. Su, W. Zhang, S. Zhao, Fault prediction for nonlinear system using sliding ARMA combined with online LS-SVR, Mathematical Problems in Engineering 2014 (2014) 9.
[108]J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, B.D. Moor, J. Vandewalle, Least squares support vector machines, World Scientific, Singapore, 2002.
[109]S. Talatahari, A.H. Gandomi, G.J. Yun, Optimum design of tower structures using firefly algorithm, The Structural Design of Tall and Special Buildings 23 (5) (2014) 350-361.
[110]X.-h. Tan, W.-h. Bi, X.-l. Hou, W. Wang, Reliability analysis using radial basis function networks and support vector machines, Computers and Geotechnics 38 (2) (2011) 178-186.
[111]Z. Tan, J. Zhang, J. Wang, J. Xu, Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models, Applied Energy 87 (11) (2010) 3606-3610.
[112]D. Tang, S. Dong, X. Cai, J. Zhao, A two-stage quantum-behaved particle swarm optimization with skipping search rule and weight to solve continuous optimization problem, Neural Computing and Applications (2015) 1-12.
[113]F.H. Triguero, Statistical Inference in Computational Intelligence and Data Mining, Vol. 2016, 2016.
[114]M.-S. Tsai, Y.-H. Lin, Modern development of an adaptive non-intrusive appliance load monitoring system in electricity energy conservation, Applied Energy 96 (2012) 55-73.
[115]F.-M. Tseng, G.-H. Tzeng, A fuzzy seasonal ARIMA model for forecasting, Fuzzy Sets and Systems 126 (3) (2002) 367-376.
[116]Vapnik V. N., The nature of statistical learning theory, Springer-Verlag, New York, New York, United States, 1995.
[117]Y. Wang, J. Wang, G. Zhao, Y. Dong, Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China, Energy Policy 48 (2012) 284-294.
[118]E. Weeks, T.H. Solomon, J. Urbach, H. Swinney, Observation of anomalous diffusion and Lévy flights, in: M. Shlesinger, G. Zaslavsky, U. Frisch (Eds.), Lévy Flights and Related Topics in Physics, Vol. 450, Springer Berlin Heidelberg, 1995, pp. 51-71.
[119]X.-K. Wei, Y.-H. Li, Y.-F. Li, D.-F. Zhang, Enclosing machine learning: concepts and algorithms, Neural Computing and Applications 17 (3) (2008) 237-243.
[120]M. Wissner, The smart grid – a saucerful of secrets?, Applied Energy 88 (7) (2011) 2509-2518.
[121]X. Xue, S. Wang, Y. Sun, F. Xiao, An interactive building power demand management strategy for facilitating smart grid optimization, Applied Energy 116 (2014) 297-310.
[122]W. Yaïci, E. Entchev, Adaptive neuro-fuzzy inference system modelling for performance prediction of solar thermal energy system, Renewable Energy 86 (2016) 302-315.
[123]X.-S. Yang, Chapter 2 - Analysis of Algorithms, Nature-Inspired Optimization Algorithms, Elsevier, Oxford, United Kingdom, 2014, pp. 23-44.
[124]X.-S. Yang, Chapter 8 - Firefly Algorithms, in: X.-S. Yang (Ed.), Nature-Inspired Optimization Algorithms, Elsevier, Oxford, 2014, pp. 111-127.
[125]X.-S. Yang, Firefly algorithm, Luniver Press, Bristol, UK, 2008.
[126]X.-S. Yang, Firefly Algorithm, Lévy Flights and Global Optimization, in: M. Bramer, R. Ellis, M. Petridis (Eds.), Research and Development in Intelligent Systems XXVI, Springer London, 2010, pp. 209-218.
[127]X.-S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: J. González, D. Pelta, C. Cruz, G. Terrazas, N. Krasnogor (Eds.), Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Vol. 284, Springer Berlin Heidelberg, 2010, pp. 65-74.
[128]X.-S. Yang, S. Deb, Cuckoo Search via Lévy flights, Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, 2009, pp. 210-214.
[129]J. Yuan, Z. Hu, Low carbon electricity development in China - an IRSP perspective based on super smart grid, Renewable and Sustainable Energy Reviews 15 (6) (2011) 2707-2713.
[130]G.P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50 (2003) 159-175.
[131]H. Zhang, F. Yang, Y. Li, H. Li, Predicting profitability of listed construction companies based on principal component analysis and support vector machine—Evidence from China, Automation in Construction 53 (2015) 22-28.
[132]P.G. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50 (0) (2003) 159-175.
[133]W. Zhang, S. Liu, N. Li, H. Xie, X. Li, Development forecast and technology roadmap analysis of renewable energy in buildings in China, Renewable and Sustainable Energy Reviews 49 (2015) 395-402.
[134]H.-x. Zhao, F. Magoulès, A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews 16 (6) (2012) 3586-3592.
[135]S. Zhou, Z. Wu, J. Li, X.-p. Zhang, Real-time energy control approach for smart home energy management system, Electric Power Components and Systems 42 (3-4) (2014) 315-326.