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A review on time series forecasting techniques for building energy consumption
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
Aamodt, A., & Plaza, E. (1994) · AI Communications · doi:10.3233/aic-1994-7104 · Cited by 5,624
Research works printed but not linked: 196
- IEA. Energy and climate change; 2015. 〈http://doi.org/10.1038/479267b〉. · doi:10.1038/479267b
- Al-Homoud, 2001, Computer-aided building energy analysis techniques, Build Environ, 36, 421, 10.1016/S0360-1323(00)00026-3 · DOI · Paper page
- Crawley, 2008, Contrasting the capabilities of building energy performance simulation programs, Build Environ, 43, 661, 10.1016/j.buildenv.2006.10.027 · DOI · Paper page
- Yang, 2015, Energy performance model development and occupancy number identification of institutional buildings, Energy Build
- Yang, 2014, High throughput computing based distributed genetic algorithm for building energy consumption optimization, Energy Build, 76, 92, 10.1016/j.enbuild.2014.02.053 · DOI
- Petri, 2014, A modular optimisation model for reducing energy consumption in large scale building facilities, Renew Sustain Energy Rev, 38, 990, 10.1016/j.rser.2014.07.044 · doi:10.1016/j.rser.2014.07.044
- Aste, 2016, Building automation and control systems and performance optimization: a framework for analysis, Renew Sustain Energy Rev
- Jagarajan, 2017, Green retrofitting – a review of current status, implementations and challenges, Renew Sustain Energy Rev, 67, 1360, 10.1016/j.rser.2016.09.091 · DOI
- Zong, 2016, Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems, Appl Therm Eng
- Hu, 2016, Building performance optimisation: a hybrid architecture for the integration of contextual information and time-series data, Autom Constr, 70, 51, 10.1016/j.autcon.2016.05.018 · doi:10.1016/j.autcon.2016.05.018
- Zhao, 2012, A review on the prediction of building energy consumption, Renew Sustain Energy Rev, 16, 3586, 10.1016/j.rser.2012.02.049 · DOI · Paper page
- Hippert, 2001, Neural networks for short-term load forecasting: a review and evaluation, IEEE Trans Power Syst, 16, 44, 10.1109/59.910780 · DOI · Paper page
- Suganthi, 2012, Energy models for demand forecasting – a review, Renew Sustain Energy Rev, 16, 1223, 10.1016/j.rser.2011.08.014 · DOI · Paper page
- Fumo, 2014, A review on the basics of building energy estimation, Renew Sustain Energy Rev, 31, 53, 10.1016/j.rser.2013.11.040 · DOI · Paper page
- Martínez-Álvarez, 2015, A survey on data mining techniques applied to electricity-related time series forecasting, Energies, 8, 13162, 10.3390/en81112361 · DOI · Paper page
- Raza, 2015, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings, Renew Sustain Energy Rev, 50, 1352, 10.1016/j.rser.2015.04.065 · DOI · Paper page
- Mat Daut, 2016, Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: a review, Renew Sustain Energy Rev
- Ahmad, 2014, A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renew Sustain Energy Rev, 33, 102, 10.1016/j.rser.2014.01.069 · DOI · Paper page
- Wang, 2016, A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models, Renew Sustain Energy Rev
- Morris, 1949, 50, 437
- Adya M, Collopy F How effective are neural networks at forecasting and prediction? A review and evaluation; n.d.
- Nasr GE, Badr EA, Younes MR. Neural networks in forecasting electrical energy consumption. FLAIRS-01 .In: Proc., American association for artificial intelligence; 2001.
- Nizami, 1995, Forecasting electric energy consumption using neural networks, Energy Policy, 23, 1097, 10.1016/0301-4215(95)00116-6 · DOI
- González, 2005, Prediction of hourly energy consumption in buildings based on a feedback artificial neural network, Energy Build, 37, 595, 10.1016/j.enbuild.2004.09.006 · DOI
- G. Schenker, Prediction and control using feedback neural networks and partial models. Ph.D. dissertation, Swiss Federal Institute of Technology, Zurich, 1996.
- Panapakidis IP, Dagoumas AS. Day-ahead electricity price forecasting via the application of artificial neural network based models; 2016. 〈http://doi.org/10.1016/j.apenergy.2016.03.089〉. · DOI · Paper page
- Karatasou, 2006, Modeling and predicting building's energy use with artificial neural networks: methods and results, Energy Build, 38, 949, 10.1016/j.enbuild.2005.11.005 · DOI
- Rivals, 2003, Neural-network construction and selection in nonlinear modeling, IEEE Trans Neural Netw, 14, 804, 10.1109/TNN.2003.811356 · DOI
- Azadeh, 2008, Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors, Energy Convers Manag, 49, 2272, 10.1016/j.enconman.2008.01.035 · DOI
- Ekonomou, 2010, Greek long-term energy consumption prediction using artificial neural networks, Energy, 35, 512, 10.1016/j.energy.2009.10.018 · DOI · Paper page
- Neto, 2008, Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption, Energy Build, 40, 2169, 10.1016/j.enbuild.2008.06.013 · DOI · Paper page
- Yokoyama, 2009, Prediction of energy demands using neural network with model identification by global optimization, Energy Convers Manag, 50, 319, 10.1016/j.enconman.2008.09.017 · DOI
- Yokoyama, 2005, Capability of global search and improvement in modal trimming method for global optimization, JSME Int J Ser C, 48, 730, 10.1299/jsmec.48.730 · doi:10.1299/jsmec.48.730
- Rezaeian-Zadeh, 2012, Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions, Theor Appl Clim, 109, 519, 10.1007/s00704-012-0595-0 · doi:10.1007/s00704-012-0595-0
- Pao, 2006, Comparing linear and nonlinear forecasts for Taiwan's electricity consumption, Energy, 31, 2129, 10.1016/j.energy.2005.08.010 · DOI
- Hamzaçebi, 2007, Forecasting of Turkey's net electricity energy consumption on sectoral bases, Energy Policy, 35, 2009, 10.1016/j.enpol.2006.03.014 · DOI
- Kelo, 2012, A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature, Int J Electr Power Energy Syst, 43, 1063, 10.1016/j.ijepes.2012.06.009 · doi:10.1016/j.ijepes.2012.06.009
- Chitsaz, 2015, Short-term electricity load forecasting of buildings in microgrids, Energy Build, 99, 50, 10.1016/j.enbuild.2015.04.011 · DOI
- Deb, 2015, Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks, Energy Build
- Deb, 2015, Forecasting energy consumption of institutional buildings in Singapore, Procedia Eng, 121, 1734, 10.1016/j.proeng.2015.09.144 · doi:10.1016/j.proeng.2015.09.144
- Chae, 2016, Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings, Energy Build, 111, 184, 10.1016/j.enbuild.2015.11.045 · DOI
- Chae, 2016, Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings, Energy Build, 111, 184, 10.1016/j.enbuild.2015.11.045 · DOI
- Newsham, 2010, Building-level occupancy data to improve ARIMA-based electricity use forecasts, BuildSys, 2010, 10.1145/1878431.1878435 · DOI
- Yao, 2004, Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy Process, Int J Therm Sci, 43, 1107, 10.1016/j.ijthermalsci.2004.02.009 · DOI
- Saaty, 1987, The analytic hierarchy process–what it is and how it is used, Math Model, 9, 161, 10.1016/0270-0255(87)90473-8 · DOI · Paper page
- Wang, 2012, A hybrid neural network and ARIMA model for energy consumption forcasting, J Comput, 7, 1184
- Kandananond, 2011, Forecasting electricity demand in Thailand with an artificial neural network approach, Energies, 4, 1246, 10.3390/en4081246 · doi:10.3390/en4081246
- Chujai, 2013, Time series analysis of household electric consumption with ARIMA and ARMA models, IMECS, 2013
- Zhuang J, Chen Y, Shi X, Wei D. Building cooling load prediction based on time series method and neural networks. Vol. 8; 2015, p. 105–14. · doi:10.14257/ijgdc.2015.8.4.10
- Abdel-Aal, 1997, Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis, Energy, 22, 1059, 10.1016/S0360-5442(97)00032-7 · DOI
- Abosedra, 2009, Demand for electricity in Lebanon, Int Bus Econ Res J, 8, 11
- Almeshaiei, 2011, A methodology for electric power load forecasting, Alex Eng J, 50, 137, 10.1016/j.aej.2011.01.015 · DOI · Paper page
- Dikaios, 1992, Forecasting residential electricity consumption in Greece using monthly and quarterly data, Energy Econ, 14, 226, 10.1016/0140-9883(92)90016-7 · doi:10.1016/0140-9883(92)90016-7
- Erdogdu, 2007, Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey, Energy Policy, 35, 1129, 10.1016/j.enpol.2006.02.013 · doi:10.1016/j.enpol.2006.02.013
- Twanabasu SR, Bremdal BA. Load forecasting in a smart grid oriented building. In: 22nd int. conf. exhib. electr. distrib. (CIRED 2013), Institution of Engineering and Technology; 2013, p. 0907–0907. · doi:10.1049/cp.2013.0997
- Katara, 2014, A time series analysis of electricity demand in Tamale, Ghana, 4, 269
- Mohamed, 2011, Improving short term load forecasting using double seasonal arima model, Appl Sci, 15, 223
- Godfred Kwame, 2013, Modeling and forecasting energy consumption in Ghana, J Energy Technol Policy, 3, 1
- Rallapalli, 2012, Forecasting monthly peak demand of electricity in India—a critique, Energy Policy, 45, 516, 10.1016/j.enpol.2012.02.064 · doi:10.1016/j.enpol.2012.02.064
- Wang, 2012, Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China, Energy Policy, 48, 284, 10.1016/j.enpol.2012.05.026 · DOI
- Yasmeen, 2014, Forecasting electricity consumption for Pakistan, Int J Emerg Technol Adv Eng, 4, 496
- Vapnik, 1996, A support vector method for function approximation, regression estimation, and signal processing, Adv Neural Inf Process Syst, 9, 281
- Zhang, 2016, Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique, Energy Build, 126, 94, 10.1016/j.enbuild.2016.05.028 · DOI · Paper page
- Chen, 2004, Load forecasting using support vector machines: a study on EUNITE competition 2001, Power Syst IEEE Trans, 19, 1821, 10.1109/TPWRS.2004.835679 · DOI · Paper page
- Hong, 2009, Electric load forecasting by support vector model, Appl Math Model, 33, 2444, 10.1016/j.apm.2008.07.010 · DOI · Paper page
- Pai, 2005, Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Convers Manag, 46, 2669, 10.1016/j.enconman.2005.02.004 · DOI · Paper page
- Dong, 2005, Applying support vector machines to predict building energy consumption in tropical region, Energy Build, 37, 545, 10.1016/j.enbuild.2004.09.009 · DOI · Paper page
- Li, 2009, Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks, Energy Convers Manag, 50, 90, 10.1016/j.enconman.2008.08.033 · DOI
- Li, 2009, Applying support vector machine to predict hourly cooling load in the building, Appl Energy, 86, 2249, 10.1016/j.apenergy.2008.11.035 · DOI · Paper page
- Xuemei, 2009, Building cooling load forecasting model based on LS-SVM, Asia-Pac Conf Inf Process, 1, 55
- Božic, 2010, Short-term electric load forecasting using least square support vector machines, Autom Control Robot, 9, 141
- Fernandez I, Borges CE, Penya YK. Efficient building load forecasting. ETFA2011, IEEE; 2011, p. 1–8. · doi:10.1109/etfa.2011.6059103
- Edwards, 2012, Predicting future hourly residential electrical consumption: a machine learning case study, Energy Build, 49, 591, 10.1016/j.enbuild.2012.03.010 · DOI · Paper page
- Oğcu, 2012, Forecasting electricity consumption with neural networks and support vector regression, Procedia – Soc Behav Sci, 58, 1576, 10.1016/j.sbspro.2012.09.1144 · DOI
- Zhao, 2012, The application of support vector machine in load forecasting, J Comput, 7, 1615, 10.4304/jcp.7.7.1615-1622 · doi:10.4304/jcp.7.7.1615-1622
- Ceperic, 2013, A strategy for short-term load forecasting by support vector regression machines, IEEE Trans Power Syst, 1
- Jain, 2014, Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy, Appl Energy, 123, 168, 10.1016/j.apenergy.2014.02.057 · DOI · Paper page
- Kaytez, 2015, Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines, Int J Electr Power Energy Syst, 67, 431, 10.1016/j.ijepes.2014.12.036 · DOI · Paper page
- Liu D, Chen Q, Mori K. Time series forecasting method of building energy consumption using support vector regression. In: International conference inf. autom., Lijiang, China; 2015, p. 1628–32. · doi:10.1109/icinfa.2015.7279546
- Fu, 2015, Using support vector machine to predict next day electricity load of public buildings with sub-metering devices, Procedia Eng, 121, 1016, 10.1016/j.proeng.2015.09.097 · DOI
- Monfet D, Arkhipova E, Choiniere D Evaluation of a case-based reasoning energy prediction tool for commercial buildings. In: Proceedings of the 13th international conference enhanc. build. oper., Montreal, Quebec, Canada; 2013, p. 1–7.
- Monfet, 2014, Development of an energy prediction tool for commercial buildings using case-based reasoning, Energy Build, 81, 152, 10.1016/j.enbuild.2014.06.017 · DOI
- Song, 1993, Fuzzy time series and its models, Fuzzy Sets Syst, 54, 269, 10.1016/0165-0114(93)90372-O · doi:10.1016/0165-0114(93)90372-o
- Azadeh, 2010, An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran, Energy, 35, 2351, 10.1016/j.energy.2009.12.023 · DOI
- Azadeh A, Seraj O, Saberi MA. Total fuzzy regression algorithm for energy consumption estimation. 2008 In: Proceedings of the 6th IEEE international conference ind informatics; 2008, p. 1562–8. 〈http://doi.org/10.1109/INDIN.2008.4618353〉. · doi:10.1109/indin.2008.4618353
- Bolturk E, Oztaysi B, Sari IU. Electricity consumption forecasting using fuzzy time series. In: 2012 IEEE 13th international symp. comput. intell. informatics, IEEE; 2012, p. 245–249. · doi:10.1109/cinti.2012.6496768
- Singh, 2007, A simple method of forecasting based on fuzzy time series, Appl Math Comput, 186, 330
- Efendi, 2015, A new linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand, Appl Soft Comput J, 28, 422, 10.1016/j.asoc.2014.11.043 · doi:10.1016/j.asoc.2014.11.043
- Lim Y-D, Kim S-C, Park J-O, Choo Y-G. A predictive demand of the maximum electric power using chaos-fuzzy. In: Proceedings of the 10th IEEE international conference fuzzy syst. (Cat. no.01CH37297). Vol. 2, IEEE; 2001, p. 1511–1514. · doi:10.1109/fuzz.2001.1008949
- Liu X, Bai E, Fang J, Luo L. Time-variant slide fuzzy time-series method for short-term load forecasting. In: Proceedings 2010 IEEE international conference intell comput intell syst ICIS; 2010, p. 1:65–8. · doi:10.1109/icicisys.2010.5658722
- Pei AO. Load forecasting based on fuzzy time series. In: Proceedings of the 3rd international conference mater. mech. manuf. eng. (IC3ME 2015), Guangzhou, China; 2015, p. 715–9. · doi:10.2991/ic3me-15.2015.139
- Otto P, Schunk T. Fuzzy based time series forecasting of electric load. In: Eur. control conference, Karlsruhe, Germany; 1999, p. 4799–804. · doi:10.23919/ecc.1999.7100095
- Ozawa K, Niimura T, Nakashima T. Fuzzy time-series model of electric power consumption. In: IEEE can. conference electr. comput. eng., Alberta, Canada; 1999, p. 1195–8. 〈http://doi.org/10.1109/CCECE.1999.808235〉. · doi:10.1109/ccece.1999.808235
- Tanaka, 1982, Linear regression analysis with fuzzy model, IEEE Trans Syst Man Cyber, 12, 903, 10.1109/TSMC.1982.4308925 · DOI · Paper page
- Pereira, 2015, Fuzzy modeling to forecast an electric load time series, Procedia Comput Sci, 55, 395, 10.1016/j.procs.2015.07.089 · doi:10.1016/j.procs.2015.07.089
- Thiang YK. Electrical load time series data forecasting using interval type-2 fuzzy logic system. In: Proceedings of the 3rd international conference comput. sci. inf. technol., Vol. 5, IEEE; 2010, p. 527–531. · doi:10.1109/iccsit.2010.5563903
- Deng, 1982, Control problems of grey systems, Syst Control Lett, 1, 288, 10.1016/S0167-6911(82)80025-X · DOI · Paper page
- Deng, 1989, Introduction to grey system theory, J Grey Syst, 1, 1
- Jiang, 2004, Applying grey forecasting to predicting the operating energy performance of air cooled water chillers, Int J Refrig, 27, 385, 10.1016/j.ijrefrig.2003.12.001 · DOI
- Lee, 2011, Forecasting energy consumption using a grey model improved by incorporating genetic programming, Energy Convers Manag, 52, 147, 10.1016/j.enconman.2010.06.053 · doi:10.1016/j.enconman.2010.06.053
- Yao, 2003, An improved grey-based approach for electricity demand forecasting, Electr Power Syst Res, 67, 217, 10.1016/S0378-7796(03)00112-3 · doi:10.1016/s0378-7796(03)00112-3
- Yao, 2004, Analysis and design of a Taguchi–Grey based electricity demand predictor for energy management systems, Energy Convers Manag, 45, 1205, 10.1016/j.enconman.2003.08.008 · doi:10.1016/j.enconman.2003.08.008
- Akay, 2007, Grey prediction with rolling mechanism for electricity demand forecasting of Turkey, Energy, 32, 1670, 10.1016/j.energy.2006.11.014 · DOI
- Pao, 2012, Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model, Energy, 40, 400, 10.1016/j.energy.2012.01.037 · DOI
- Zhou, 2008, A grey-box model of next-day building thermal load prediction for energy-efficient control, Int J Energy Res, 32, 1418, 10.1002/er.1458 · DOI
- Hsu, 2003, Applications of improved grey prediction model for power demand forecasting, Energy Convers Manag, 44, 2241, 10.1016/S0196-8904(02)00248-0 · DOI · Paper page
- Bianco, 2010, Analysis and forecasting of nonresidential electricity consumption in Romania, Appl Energy, 87, 3584, 10.1016/j.apenergy.2010.05.018 · DOI
- Hamzacebi, 2014, Forecasting the annual electricity consumption of Turkey using an optimized grey model, Energy, 70, 165, 10.1016/j.energy.2014.03.105 · doi:10.1016/j.energy.2014.03.105
- Jin, 2012, Short-term power load forecasting using grey correlation contest modeling, Expert Syst Appl, 39, 773, 10.1016/j.eswa.2011.07.072 · doi:10.1016/j.eswa.2011.07.072
- Kang, 2012, Application of improved grey model in long-term load forecasting of power engineering, Syst Eng Procedia, 3, 85, 10.1016/j.sepro.2011.11.012 · doi:10.1016/j.sepro.2011.11.012
- Zhou, 2006, A trigonometric grey prediction approach to forecasting electricity demand, Energy, 31, 2839, 10.1016/j.energy.2005.12.002 · DOI
- Li, 2011, A research on short term load forecasting problem applying improved grey dynamic model, Int J Electr Power Energy Syst, 33, 809, 10.1016/j.ijepes.2010.11.005 · doi:10.1016/j.ijepes.2010.11.005
- Li G, Yamaguchi D, Nagai M. Application of improved grey prediction model to short term load forecasting. In: International conference electr. eng.; 2006, p. 1–6.
- Winters, 1960, Forecasting sales by exponentially weighted moving averages, Manag Sci, 6, 324, 10.1287/mnsc.6.3.324 · DOI · Paper page
- Christiaanse, 1971, Short-term load forecasting using general exponential smoothing, IEEE Trans Power Appar Syst, 900, 10.1109/TPAS.1971.293123 · DOI · Paper page
- Taylor, 2006, A comparison of univariate methods for forecasting electricity demand up to a day ahead, Int J Forecast, 22, 1, 10.1016/j.ijforecast.2005.06.006 · DOI
- Taylor, 2008, An evaluation of methods for very short-term load forecasting using minute-by-minute British data, Int J Forecast, 24, 645, 10.1016/j.ijforecast.2008.07.007 · doi:10.1016/j.ijforecast.2008.07.007
- Taylor, 2010, Triple seasonal methods for short-term electricity demand forecasting, Eur J Oper Res, 204, 139, 10.1016/j.ejor.2009.10.003 · DOI · Paper page
- Kotillová, 2011, Very short-term load forecasting using exponential smoothing and Arima models, J Inf, Control Manag, 9, 85
- Taylor, 2012, Short-term load forecasting with exponentially weighted methods, IEEE Trans Power Syst, 27, 458, 10.1109/TPWRS.2011.2161780 · doi:10.1109/tpwrs.2011.2161780
- Adilah, 2013, Electricity load demand forecasting using exponential smoothing methods, World Appl Sci J, 22, 1540
- Ariffin, 2013, Electricity load forecasting in UTP using moving averages and exponential smoothing techniques, Appl Math Sci, 7, 4003
- Maçaira, 2015, Modelling and forecasting the residential electricity consumption in Brazil with pegels exponential smoothing techniques, Procedia Comput Sci, 55, 328, 10.1016/j.procs.2015.07.057 · doi:10.1016/j.procs.2015.07.057
- Lora AT, Santos JMR, Riquelme JC, Exposito AG, Ramos JLM. Time-series prediction: application to the short-term electric energy demand. In: 10th Conf. Spanish Assoc. Artif. Intell. (CAEPIA), 5th Conf. Technol. Transf., Albacete, Spain; 2004, p. 577–86. · doi:10.1007/978-3-540-25945-9_57
- Lei S, Sun C, Zhou Q, Zhang X. The research of local linear model of short term electrical load on multivariate time series. In: 2005 IEEE russ power tech; 2005, p. 1–5. 〈http://doi.org/10.1109/PTC.2005.4524543〉. · doi:10.1109/ptc.2005.4524543
- Brown, 2012, Kernel regression for real-time building energy analysis, J Build, 5, 263
- Al-Qahtani FH, Crone SF. Multivariate k-nearest neighbour regression for time series data — a novel algorithm for forecasting UK electricity demand. In: 2013 International Jt. conference neural networks, Dallas, USA; 2013, p. 1–8. 〈http://doi.org/10.1109/IJCNN.2013.6706742〉. · DOI
- Lachut D, Banerjee N, Rollins S. Predictability of energy use in homes. In: Proceedings international green comput. conference (IGCC 14), Dallas, USA; 2014, p. 1–10. 〈http://doi.org/10.1109/IGCC.2014.7039146〉. · DOI
- Roger, 2014, Improved electric power demand forecasting by adapting the weighted average to the MISMO strategy, Int J Electr Eng Inform, 6, 237, 10.15676/ijeei.2014.6.2.2 · doi:10.15676/ijeei.2014.6.2.2
- Colombo T, Koprinska I, Panella M. Maximum length weighted nearest neighbor approach for electricity load forecasting. In: International jt. conference neural networks, Killarney, Ireland; 2015. · doi:10.1109/ijcnn.2015.7280809
- Azadeh, 2007, Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption, Appl Math Comput, 186, 1731
- El-naggar, 2007, Algorithm, optimal filter estimator and least error squares technique: comparative study, Int’l J Electr Robot Electron Commun Eng, 1, 941
- Bashir, 2009, Applying wavelets to short-term load forecasting using PSO-based neural networks, IEEE Trans Power Syst, 24, 20, 10.1109/TPWRS.2008.2008606 · DOI · Paper page
- Lee, 2009, Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm, Neurocomputing, 73, 449, 10.1016/j.neucom.2009.07.005 · DOI
- Donate, 2011, Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm, Neural Comput Appl, 11
- Xue, 2012, Building an integrated hybrid model for short-term and mid-term load foreacsting with genetic optimization, Int J Innov Comput Inf Control, 8, 7381
- Kouhi, 2014, A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection, Int J Electr Power Energy Syst, 62, 862, 10.1016/j.ijepes.2014.05.036 · DOI
- Li, 2015, Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis, Energy Build, 108, 106, 10.1016/j.enbuild.2015.09.002 · DOI · Paper page
- Wang, 2014, Techniques of applying wavelet transform into combined model for short-term load forecasting, Electr Power Energy Syst, 62, 816, 10.1016/j.ijepes.2014.05.038 · doi:10.1016/j.ijepes.2014.05.038
- Zhuang, 2015, Building cooling load prediction based on time series method and neural networks, Int J Grid Distrib Comput, 8, 105, 10.14257/ijgdc.2015.8.4.10 · doi:10.14257/ijgdc.2015.8.4.10
- Hong-Tzer, 1996, Identification of ARMAX model for short term load forecasting: an evolutionary programming approach, IEEE Trans Power Syst, 11, 403, 10.1109/59.486125 · DOI
- Chao-Ming, 2005, A particle swarm optimization to identifying the ARMAX model for short-term load forecasting, Power Syst IEEE Trans, 20, 1126, 10.1109/TPWRS.2005.846106 · DOI · Paper page
- Wang, 2008, A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting, Electr Power Syst Res, 78, 1679, 10.1016/j.epsr.2008.02.009 · doi:10.1016/j.epsr.2008.02.009
- Lü, 2015, Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach, Appl Energy, 144, 261, 10.1016/j.apenergy.2014.12.019 · DOI
- Nie, 2011, Hybrid of ARIMA and SVMs for short-term load forecasting, Energy Procedia, 16, 1455, 10.1016/j.egypro.2012.01.229 · doi:10.1016/j.egypro.2012.01.229
- Che, 2014, Short-term load forecasting using a kernel-based support vector regression combination model, Appl Energy, 132, 602, 10.1016/j.apenergy.2014.07.064 · DOI
- Fan, 2015, Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression, Neurocomputing, 173, 958, 10.1016/j.neucom.2015.08.051 · DOI
- Lee, 2015, A hybrid dynamic and fuzzy time series model for mid-term power load forecasting, Int J Electr Power Energy Syst, 64, 1057, 10.1016/j.ijepes.2014.08.006 · doi:10.1016/j.ijepes.2014.08.006
- Pao, 2009, Forecasting energy consumption in Taiwan using hybrid nonlinear models, Energy, 34, 1438, 10.1016/j.energy.2009.04.026 · DOI
- Li, 2014, Application of a hybrid quantized Elman neural network in short-term load forecasting, Int J Electr Power Energy Syst, 55, 749, 10.1016/j.ijepes.2013.10.020 · DOI
- Mao, 2009, Short and mid-term load forecasting using a bilevel optimization model, IEEE Trans Power Syst, 24, 1080, 10.1109/TPWRS.2009.2016609 · DOI
- Chang, 2011, Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach, Int J Electr Power Energy Syst, 33, 17, 10.1016/j.ijepes.2010.08.008 · DOI
- Chen, 2012, Long-term load forecasting by a collaborative fuzzy-neural approach, Int J Electr Power Energy Syst, 43, 454, 10.1016/j.ijepes.2012.05.072 · doi:10.1016/j.ijepes.2012.05.072
- Chaturvedi, 2015, Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network, Electr Power Energy Syst, 67, 230, 10.1016/j.ijepes.2014.11.027 · DOI
- Hong-Tzer, 1998, A new short-term load forecasting approach using self-organizing fuzzy armax models, IEEE Trans Power Syst, 13, 217, 10.1109/59.651639 · doi:10.1109/59.651639
- Azadeh, 2008, Improved estimation of electricity demand function by integration of fuzzy system and data mining approach, Energy Convers Manag, 49, 2165, 10.1016/j.enconman.2008.02.021 · doi:10.1016/j.enconman.2008.02.021
- Enayatifar, 2013, Imperialist competitive algorithm combined with refined high-order weighted fuzzy time series (RHWFTS–ICA) for short term load forecasting, Energy Convers Manag, 76, 1104, 10.1016/j.enconman.2013.08.039 · DOI
- Sadaei, 2014, Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search, Int J Electr Power Energy Syst, 62, 118, 10.1016/j.ijepes.2014.04.026 · DOI
- Che, 2012, An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting, Energy, 37, 657, 10.1016/j.energy.2011.10.034 · DOI
- Son, 2015, Forecasting short-term electricity demand in residential sector based on support vector regression and fuzzy-rough feature selection with particle swarm optimization, Procedia Eng, 118, 1162, 10.1016/j.proeng.2015.08.459 · doi:10.1016/j.proeng.2015.08.459
- Li F, Zhao X. The application of genetic algorithm in power short-term load forecasting; 2012, p. 50. 〈http://doi.org/10.7763/IPCSIT.2012.V50.28〉. · doi:10.7763/ipcsit.2012.v50.28〉
- Wei C-C, Chen T-T, Lee S-J. k-NN based neuro-fuzzy system for time series prediction. In: Proceedings of the 14th ACIS international conference softw eng artif intell netw parallel/distributed comput; 2013, p. 569–74. 〈http://doi.org/10.1109/SNPD.2013.68〉. · doi:10.1109/snpd.2013.68
- Sorjamaa, 2007, Methodology for long-term prediction of time series, Neurocomputing, 70, 2861, 10.1016/j.neucom.2006.06.015 · DOI · Paper page
- Chen, 2015, A weighted LS-SVM based learning system for time series forecasting, Inf Sci, 299, 99, 10.1016/j.ins.2014.12.031 · doi:10.1016/j.ins.2014.12.031
- Azadeh, 2009, A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation, Expert Syst Appl, 36, 11108, 10.1016/j.eswa.2009.02.081 · doi:10.1016/j.eswa.2009.02.081
- Akdemir, 2012, Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data, Energy Procedia, 14, 794, 10.1016/j.egypro.2011.12.1013 · doi:10.1016/j.egypro.2011.12.1013
- Hooshmand, 2013, A hybrid intelligent algorithm based short-term load forecasting approach, Int J Electr Power Energy Syst, 45, 313, 10.1016/j.ijepes.2012.09.002 · DOI
- Wu, 2013, Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model, Energy Convers Manag, 70, 1, 10.1016/j.enconman.2013.02.010 · doi:10.1016/j.enconman.2013.02.010
- Cao, 2003, Support vector machines experts for time series forecasting, Neurocomputing, 51, 321, 10.1016/S0925-2312(02)00577-5 · DOI · Paper page
- Pai, 2005, Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms, Electr Power Syst Res, 74, 417, 10.1016/j.epsr.2005.01.006 · DOI · Paper page
- Hong, 2009, Hybrid evolutionary algorithms in a SVR-based electric load forecasting model, Int J Electr Power Energy Syst, 31, 409, 10.1016/j.ijepes.2009.03.020 · DOI
- Hong, 2009, Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model, Energy Convers Manag, 50, 105, 10.1016/j.enconman.2008.08.031 · DOI · Paper page
- Li X, Ding L, Li L. A novel building cooling load prediction based on SVR and SAPSO. In: 3CA 2010–2010 international symp comput commun control autom. Vol. 1; 2010, p. 528–532. 〈http://doi.org/10.1109/3CA.2010.5533863〉. · DOI
- Niu, 2010, Power load forecasting using support vector machine and ant colony optimization, Expert Syst Appl, 37, 2531, 10.1016/j.eswa.2009.08.019 · DOI · Paper page
- Wang, 2010, Optimizing of SVM with hybrid PSO and genetic algorithm in power load forecasting, J Netw, 5, 1192
- Hong, 2011, Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm, Energy, 36, 5568, 10.1016/j.energy.2011.07.015 · DOI · Paper page
- Zhang, 2012, Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting, Energy, 45, 850, 10.1016/j.energy.2012.07.006 · DOI
- Ju, 2013, Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting, Appl Math Model, 37, 9643, 10.1016/j.apm.2013.05.016 · DOI
- Hong, 2013, Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm, Int J Electr Power Energy Syst, 44, 604, 10.1016/j.ijepes.2012.08.010 · DOI · Paper page
- Kavousi-Fard, 2014, A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting, Expert Syst Appl, 41, 6047, 10.1016/j.eswa.2014.03.053 · DOI · Paper page
- Chen, 2014, A hybrid application algorithm based on the support vector machine and artificial intelligence: an example of electric load forecasting, Appl Math Model, 39, 2617, 10.1016/j.apm.2014.10.065 · DOI
- Baziar A, Kavousi-fard A. Short term load forecasting using a hybrid model based on support vector regression. Vol. 4; 2015, p.189–95.
- Ghofrani, 2015, A hybrid short-term load forecasting with a new input selection framework, Energy, 81, 777, 10.1016/j.energy.2015.01.028 · DOI
- Kouhi, 2013, A new cascade NN based method to short-term load forecast in deregulated electricity market, Energy Convers Manag, 71, 76, 10.1016/j.enconman.2013.03.014 · doi:10.1016/j.enconman.2013.03.014
- Moazzami, 2013, A new hybrid day-ahead peak load forecasting method for Iran's National grid, Appl Energy, 101, 489, 10.1016/j.apenergy.2012.06.009 · doi:10.1016/j.apenergy.2012.06.009
- Meng, 2011, Forecasting monthly electric energy consumption using feature extraction, Energies, 4, 1495, 10.3390/en4101495 · doi:10.3390/en4101495
- Sudheer, 2015, Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models, Int J Electr Power Energy Syst, 64, 340, 10.1016/j.ijepes.2014.07.043 · doi:10.1016/j.ijepes.2014.07.043
- Bahrami, 2014, Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm, Energy, 72, 434, 10.1016/j.energy.2014.05.065 · DOI · Paper page
- Magnier, 2010, Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network, Build Environ, 45, 739, 10.1016/j.buildenv.2009.08.016 · DOI · Paper page
- Kalogirou, 2000, Applications of artificial neural-networks for energy systems, Appl Energy, 67, 17, 10.1016/S0306-2619(00)00005-2 · DOI
- Labeodan, 2016, On the application of wireless sensors and actuators network in existing buildings for occupancy detection and occupancy-driven lighting control, Energy Build, 127, 75, 10.1016/j.enbuild.2016.05.077 · DOI
- Kumar, 2016, Real-time sensors for indoor air monitoring and challenges ahead in deploying them to urban buildings, Sci Total Environ, 560, 150, 10.1016/j.scitotenv.2016.04.032 · DOI · Paper page
- Lilis, 2017, Towards the next generation of intelligent building: an assessment study of current automation and future IoT based systems with a proposal for transitional design, Sustain Cities Soc, 28, 473, 10.1016/j.scs.2016.08.019 · DOI
- Chen, 2016, Cost-benefit evaluation for building intelligent systems with special consideration on intangible benefits and energy consumption, Energy Build, 128, 484, 10.1016/j.enbuild.2016.07.021 · DOI
- Kazmi, 2014, A review of wireless-sensor-network-enabled building energy management systems, ACM Trans Sens Netw, 10, 1, 10.1145/2532644 · DOI
- Kuzlu M, Pipattanasomporn M, Rahman S. Review of communication technologies for smart homes/building applications. In: 2015 IEEE innov. smart grid technol. – Asia (ISGT ASIA), IEEE; 2015, p. 1–6. 〈http://doi.org/10.1109/ISGT-Asia.2015.7437036〉. · doi:10.1109/isgt-asia.2015.7437036
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Metadata
- Authors
- Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 74
- Pages
- 902-924
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2592453717
- Cited by
- 1,006
- References
- 5
- FWCI
- 68.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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