Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

Applied Energy

Volumn 177, Issue , 2016, Pages 751-770

  Chou, Jui Sheng ^a   Ngo, Ngoc Tri ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Building energy management; Energy consumption; Machine learning; Metaheuristic optimization; Pattern prediction; Smart grid data; Time series technique

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; BUILDINGS; ELECTRIC POWER TRANSMISSION NETWORKS; ENERGY CONSERVATION; ENERGY EFFICIENCY; ENERGY UTILIZATION; FORECASTING; LEARNING SYSTEMS; OPTIMIZATION; TIME SERIES;

BUILDING ENERGY CONSUMPTION; BUILDING ENERGY EFFICIENCY; BUILDING ENERGY MANAGEMENTS; LEAST SQUARES SUPPORT VECTOR REGRESSION; META-HEURISTIC OPTIMIZATIONS; SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGES; SMART GRID; TIME SERIES TECHNIQUES;

SMART POWER GRIDS;

ALGORITHM; ENERGY EFFICIENCY; ENERGY USE; ERROR ANALYSIS; FORECASTING METHOD; MACHINE LEARNING; OPTIMIZATION; PREDICTION; TIME SERIES ANALYSIS;

EID: 84973111565     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.05.074     Document Type: Article

References (70)

1. Yuan J., Hu Z. Low carbon electricity development in China - an IRSP perspective based on super smart grid. Renew Sustain Energy Rev 2011, 15:2707-2713.
2. Grid 2030 - a national vision for electricity's second 100 years. United States Department of Energy; 2003.
3. Crisostomi E., Gallicchio C., Micheli A., Raugi M., Tucci M. Prediction of the Italian electricity price for smart grid applications. Neurocomputing 2015, 170:286-295.
4. Di Santo K.G., Kanashiro E., Di Santo S.G., Saidel M.A. A review on smart grids and experiences in Brazil. Renew Sustain Energy Rev 2015, 52:1072-1082.
5. Soares J., Fotouhi Ghazvini M.A., Vale Z., de Moura Oliveira P.B. A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads. Appl Energy 2016, 162:1074-1088.
6. Colak I., Fulli G., Sagiroglu S., Yesilbudak M., Covrig C.-F. Smart grid projects in Europe: current status, maturity and future scenarios. Appl Energy 2015, 152:58-70.
7. IEC. IEC Smart Rrid Standardization Roadmap Edition 1.0. SMB Smart Grid Strategic Group; 2010.
8. Kolokotsa D. The role of smart grids in the building sector. Energy Build 2016, 116:703-708.
9. Cui B., Gao D-c, Wang S., Xue X. Effectiveness and life-cycle cost-benefit analysis of active cold storages for building demand management for smart grid applications. Appl Energy 2015, 147:523-535.
10. Xue X., Wang S., Sun Y., Xiao F. An interactive building power demand management strategy for facilitating smart grid optimization. Appl Energy 2014, 116:297-310.
11. Ahmad A.S., Hassan M.Y., Abdullah M.P., Rahman H.A., Hussin F., Abdullah H., et al. A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 2014, 33:102-109.
12. Zhang W., Liu S., Li N., Xie H., Li X. Development forecast and technology roadmap analysis of renewable energy in buildings in China. Renew Sustain Energy Rev 2015, 49:395-402.
13. Hsu D. Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data. Appl Energy 2015, 160:153-163.
14. Lü X., Lu T., Kibert C.J., Viljanen M. Modeling and forecasting energy consumption for heterogeneous buildings using a physical-statistical approach. Appl Energy 2015, 144:261-275.
15. Fumo N., Rafe Biswas M.A. Regression analysis for prediction of residential energy consumption. Renew Sustain Energy Rev 2015, 47:332-343.
16. Ekici B.B., Aksoy U.T. Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw 2009, 40:356-362.
17. D'Andrea E., Lazzerini B., del Rosario S.L. Neural network-based forecasting of energy consumption due to electric lighting in office buildings. Sustain Internet ICT Sustain 2012, 10:1-5.
18. Li Q., Meng Q., Cai J., Yoshino H., Mochida A. Applying support vector machine to predict hourly cooling load in the building. Appl Energy 2009, 86:2249-2256.
19. Haydari Z, Kavehnia F, Askari M, Ganbariyan M. Time-series load modelling and load forecasting using neuro-fuzzy techniques. In: 9th International conference on electrical power quality and utilisation; 2007. p. 1-6.
20. Naji S., Shamshirband S., Basser H., Keivani A., Alengaram U.J., Jumaat M.Z., et al. Application of adaptive neuro-fuzzy methodology for estimating building energy consumption. Renew Sustain Energy Rev 2016, 53:1520-1528.
21. Guo J.J., Wu J.Y., Wang R.Z. A new approach to energy consumption prediction of domestic heat pump water heater based on grey system theory. Energy Build 2011, 43:1273-1279.
22. Hamzacebi C., Es H.A. Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy 2014, 70:165-171.
23. Box G.E.P., Jenkins G.M. Time series analysis: forecasting and control 1970, Holden-day, California, United States. 3rd ed.
24. Tan Z., Zhang J., Wang J., Xu J. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl Energy 2010, 87:3606-3610.
25. Box G.E.P., Jenkins G. Time series analysis, forecasting and control 1990, Holden-Day, Incorporated, California, United States.
26. Khashei M., Bijari M. A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 2011, 11:2664-2675.
27. Wang Y., Wang J., Zhao G., Dong Y. Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China. Energy Policy 2012, 48:284-294.
28. Pal M., Deswal S. Support vector regression based shear strength modelling of deep beams. Comput Struct 2011, 89:1430-1439.
29. Martins F.F., Begonha A., Amália Sequeira Braga M. Prediction of the mechanical behavior of the Oporto granite using Data Mining techniques. Expert Syst Appl 2012, 39:8778-8783.
30. De Boeck L., Verbeke S., Audenaert A., De Mesmaeker L. Improving the energy performance of residential buildings: a literature review. Renew Sustain Energy Rev 2015, 52:960-975.
31. Foucquier A., Robert S., Suard F., Stéphan L., Jay A. State of the art in building modelling and energy performances prediction: a review. Renew Sustain Energy Rev 2013, 23:272-288.
32. Fumo N. A review on the basics of building energy estimation. Renew Sustain Energy Rev 2014, 31:53-60.
33. Li K., Hu C., Liu G., Xue W. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy Build 2015, 108:106-113.
34. Mena R., Rodríguez F., Castilla M., Arahal M.R. A prediction model based on neural networks for the energy consumption of a bioclimatic building. Energy Build 2014, 82:142-155.
35. Chen H., Huang Y., Shen H., Chen Y., Ru M., Chen Y., et al. Modeling temporal variations in global residential energy consumption and pollutant emissions. Appl Energy 2015, 10.1016/j.apenergy.2015.10.185.
36. Fan C., Xiao F., Wang S. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques. Appl Energy 2014, 127:1-10.
37. Zhao H.-X., Magoulès F. A review on the prediction of building energy consumption. Renew Sustain Energy Rev 2012, 16:3586-3592.
38. Sandels C., Widén J., Nordström L., Andersson E. Day-ahead predictions of electricity consumption in a Swedish office building from weather, occupancy, and temporal data. Energy Build 2015, 108:279-290.
39. Zhang P.G. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003, 50:159-175.
40. Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice; 2013. https://www.otexts.org/fpp:Otexts.
41. Chen K.-Y., Wang C.-H. A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Syst Appl 2007, 32:254-264.
42. Choi T.-M., Yu Y., Au K.-F. A hybrid SARIMA wavelet transform method for sales forecasting. Decis Support Syst 2011, 51:130-140.
43. Jung H.C., Kim J.S., Heo H. Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach. Energy Build 2015, 90:76-84.
44. Lee Y.-S., Tong L.-I. Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Appl Energy 2012, 94:251-256.
45. Sánchez Lasheras F., de Cos Juez F.J., Suárez Sánchez A., Krzemień A., Riesgo Fernández P. Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resour Policy 2015, 45:37-43.
46. Khandelwal I., Adhikari R., Verma G. Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Procedia Comput Sci 2015, 48:173-179.
47. Zhang G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003, 50:159-175.
48. Hansen J.V., Nelson R.D. Time-series analysis with neural networks and ARIMA-neural network hybrids. J Exp Theor Artif Intell 2003, 15:315-330.
49. Sallehuddin R., Hj.Shamsuddin S.M. Hybrid grey relational artificial neural network and auto regressive integrated moving average model for forecasting time-series data. Appl Artif Intell 2009, 23:443-486.
50. Pai P.-F., Lin C.-S. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 2005, 33:497-505.
51. Da-yong Z, Hong-wei S, Pu C. Stock market forecasting model based on a hybrid ARMA and support vector machines. In: 15th Annual conference proceedings, international conference on management science and engineering, 2008. p. 1312-7.
52. Che J., Wang J. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling. Energy Convers Manage 2010, 51:1911-1917.
53. Su S., Zhang W., Zhao S. Fault prediction for nonlinear system using sliding ARMA combined with online LS-SVR. Math Probl Eng 2014, 2014:9.
54. Haifeng W, Dejin H. Comparison of SVM and LS-SVM for regression. In: International conference on neural networks and brain; 2005. p. 279-83.
55. Suykens J.A.K., Gestel T.V., Brabanter J.D., Moor B.D., Vandewalle J. Least squares support vector machines 2002, World Scientific, Singapore.
56. Chou J.-S., Ngo N.-T., Pham A.-D. Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression. J Comput Civil Eng 2016, 30:04015002.
57. Yang X.-S. Chapter 2 - Analysis of algorithms. Nature-inspired optimization algorithms 2014, 23-44. Elsevier, Oxford, United Kingdom.
58. Roque C.M.C., Martins P.A.L.S. Differential evolution for optimization of functionally graded beams. Compos Struct 2015, 133:1191-1197.
59. Fister I., Fister I., Yang X.-S., Brest J. A comprehensive review of firefly algorithms. Swarm Evolut Comput 2013, 13:34-46.
60. Talatahari S., Gandomi A.H., Yun G.J. Optimum design of tower structures using firefly algorithm. Struct Des Tall Spec Build 2014, 23:350-361.
61. Tseng F.-M., Tzeng G.-H. A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst 2002, 126:367-376.
62. Alwee R., Hj Shamsuddin S.M., Sallehuddin R. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators. Sci World J 2013, 2013:11.
63. Shamshirband S., Mohammadi K., Yee P.L., Petković D., Mostafaeipour A. A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation. Renew Sustain Energy Rev 2015, 52:1031-1042.
64. Raza M.Q., Khosravi A. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev 2015, 50:1352-1372.
65. Vapnik V.N. The nature of statistical learning theory 1995, Springer-Verlag, New York, New York, United States.
66. Yang X.-S. Firefly algorithm 2008, Luniver Press, Bristol, UK.
67. Coelho L.D.S., Mariani V.C. Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build 2013, 59:273-278.
68. He D., He C., Jiang L.G., Zhu H.W., Hu G.R. Chaotic characteristics of a one-dimensional iterative map with infinite collapses. IEEE Transact Circ Syst I: Fundam Theory Appl 2001, 48:900-906.
69. Chou J.-S., Ngo N.-T. Smart grid data analytics framework for increasing energy savings in residential buildings. Autom Constr 2016, 10.1016/j.autcon.2016.01.002.
70. Chou J.-S., Telaga A.S. Real-time detection of anomalous power consumption. Renew Sustain Energy Rev 2014, 33:400-411.