Short-term smart learning electrical load prediction algorithm for home energy management systems

Applied Energy

Volumn 147, Issue , 2015, Pages 10-19

  El Baz, Wessam ^a   Tzscheutschler, Peter ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Demand side management; Electrical load; Energy planning; Energy prediction; Smart home; User behavior

Indexed keywords

ALGORITHMS; AUTOMATION; BEHAVIORAL RESEARCH; ECONOMICS; ELECTRIC LOADS; ELECTRIC UTILITIES; ENERGY MANAGEMENT; FORECASTING; INTELLIGENT BUILDINGS;

DEMAND SIDE MANAGEMENTS; ELECTRICAL LOAD; ENERGY PLANNING; ENERGY PREDICTION; SMART HOMES; USER BEHAVIORS;

ENERGY MANAGEMENT SYSTEMS;

ALGORITHM; AUTOMATION; COGENERATION; COST-BENEFIT ANALYSIS; DATABASE; DEMAND-SIDE MANAGEMENT; ENERGY PLANNING; HOUSEHOLD ENERGY; MANAGEMENT PRACTICE; PREDICTION;

AUSTRIA;

EID: 84924565263     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.01.122     Document Type: Article

References (41)

1. Di Giorgio A., Pimpinella L. An event driven smart home controller enabling consumer economic saving and automated demand side management. Appl Energy 2012, 96:92-103. . 10.1016/j.apenergy.2012.02.024.
2. Gottwalt S., Ketter W., Block C., Collins J., Weinhardt C. Demand side management: a simulation of household behavior under variable prices. Energy Policy 2011, 39(12):8163-8174. . 10.1016/j.enpol.2011.10.016.
3. Warren P. A review of demand-side management policy in the UK. Renew Sustain Energy Rev 2014, 29:941-951. . 10.1016/j.rser.2013.09.009.
4. Di Giorgio A, Pimpinella L, Quaresima A, Curti S. An event driven smart home controller enabling cost effective use of electric energy and automated demand side management. In: 2011 19th mediterr conf control autom; 2011. p. 358-64. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5983014.
5. Di Giorgio A., Liberati F. Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models. Appl Energy 2014, 128:119-132. . 10.1016/j.apenergy.2014.04.032.
6. Ju SH, Lim YH, Choi MS, Baek J-M, Lee S-Y. An efficient home energy management system based on automatic meter reading. In: Int symp power line commun its appl; 2011. p. 479-84. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5764445.
7. Costanzo GT, Kheir J, Zhu G. Peak-load shaving in smart homes via online scheduling. In: 2011 IEEE int symp ind electron; 2011. p. 1347-52. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5984354.
8. Barbato A, Carpentieri G. Model and algorithms for the real time management of residential electricity demand. In: 2012 IEEE int energy conf exhib; 2012. p. 701-6. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6348242.
9. Stluka P, Godbole D, Samad T. Energy management for buildings and microgrids. In: IEEE conf decis control Eur control conf; 2011. p. 5150-7. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6161051.
10. Castillo-Cagigal M., Caamaño Martín E., Matallanas E., Masa-Bote D., Gutiérrez A., Monasterio-Huelin F., et al. PV self-consumption optimization with storage and active DSM for the residential sector. Sol Energy 2011, 85(2011):2338-2348. 10.1016/j.solener.2011.06.028.
11. Andersen F., Larsen H., Boomsma T. Long-term forecasting of hourly electricity load: identification of consumption profiles and segmentation of customers. Energy Convers Manage 2013, 68:244-252. . 10.1016/j.enconman.2013.01.018.
12. Andersen F., Larsen H., Gaardestrup R. Long term forecasting of hourly electricity consumption in local areas in Denmark. Appl Energy 2013, 110:147-162. . 10.1016/j.apenergy.2013.04.046.
13. McPherson M., Karney B. Long-term scenario alternatives and their implications: LEAP model application of Panamas electricity sector. Energy Policy 2014, 68:146-157. . 10.1016/j.enpol.2014.01.028.
14. Chen T., Wang Y.-C. Long-term load forecasting by a collaborative fuzzy-neural approach. Int J Electr Power Energy Syst 2012, 43(1):454-464. . 10.1016/j.ijepes.2012.05.072.
15. Akdemir B., Çetinkaya N. Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data. Energy Proc 2012, 14:794-799. . 10.1016/j.egypro.2011.12.1013.
16. Basu AK, Panigrahi TK, Chowdhury S, Chowdhury SP, Chakraborty N, Sinha A, Song YH. Key energy management issues of setting market clearing price (MCP) in micro-grid scenario, 2007. In: UPEC 2007., vol. 1; 2007. p. 854-60. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4469061.
17. Pollhammer K, Kupzog F, Gamauf T, Kremen M. Modeling of demand side shifting potentials for smart power grids. In: AFRICON, . . .; September 2011. p. 13-5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=%26arnumber=6072110%26isnumber=6071956.
18. Kavousi-Fard A, Samet H, Marzbani F. A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl. http://linkinghub.elsevier.com/retrieve/pii/S0957417414001912.
19. Ahmad A., Hassan M., Abdullah M., Rahman H., 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. . 10.1016/j.rser.2014.01.069.
20. Egrioglu E., Yolcu U., Aladag C.H., Bas E. Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting. Proc - Soc Behav Sci 2014, 109:1094-1100. . 10.1016/j.sbspro.2013.12.593.
21. Zúñiga K., Castilla I., Aguilar R. Using fuzzy logic to model the behavior of residential electrical utility customers. Appl Energy 2014, 115:384-393. . 10.1016/j.apenergy.2013.11.030.
22. Vas P. Artificial-intelligence-based electrical machines and drives: application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques. Monographs in Electrical and Electronic Engineering, OUP Oxford, 1999. <>. http://books.google.de/books?id=16Ai4r7qjuIC.
23. Pappas S., Ekonomou L., Karamousantas D., Chatzarakis G., Katsikas S., Liatsis P. Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models. Energy 2008, 33(9):1353-1360. . 10.1016/j.energy.2008.05.008.
24. Cassola F., Burlando M. Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Appl Energy 2012, 99:154-166. . 10.1016/j.apenergy.2012.03.054.
25. Sousa J., Neves L., Jorge H. Assessing the relevance of load profiling information in electrical load forecasting based on neural network models. Int J Electr Power Energy Syst 2012, 40(1):85-93. . 10.1016/j.ijepes.2012.02.008.
26. Kulkarni S., Simon S.P., Sundareswaran K. A spiking neural network (SNN) forecast engine for short-term electrical load forecasting. Appl Soft Comput 2013, 13(8):3628-3635. . 10.1016/j.asoc.2013.04.007.
27. Kelo S., Dudul S. Short-term Maharashtra state electrical power load prediction with special emphasis on seasonal changes using a novel focused time lagged recurrent neural network based on time delay neural network model. Expert Syst Appl 2011, 38(3):1554-1564. . 10.1016/j.eswa.2010.07.070.
28. Roldán-Blay C., Escrivá-Escrivá G., Álvarez Bel C., Roldán-Porta C., Rodríguez-García J. Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model. Energy Build 2013, 60:38-46. . 10.1016/j.enbuild.2012.12.009.
29. Escrivá-Escrivá G., Álvarez Bel C., Roldán-Blay C., Alcázar-Ortega M. New artificial neural network prediction method for electrical consumption forecasting based on building end-uses. Energy Build 2011, 43(11):3112-3119. . 10.1016/j.enbuild.2011.08.008.
30. Bakker V., Molderink A., Hurink J.L., Smit G.J.M. Domestic heat demand prediction using neural networks. 2008 19th Int conf syst eng 2008, 189-194. IEEE, http://dx.doi.org/10.1109/ICSEng.2008.51. .
31. Tzafestas S., Dalianis P., Anthopoulos G. On the overtraining phenomenon of backpropagation neural networks. Math Comput Simulat 1996, 40(5-6):507-521. . 10.1016/0378-4754(95)00003-8.
32. Datta D, Tassou S. Artificial neural network based electrical load prediction for food retail stores. Appl Therm Eng 18. http://www.sciencedirect.com/science/article/pii/S1359431198000349.
33. Mirhosseini M, Marzband M, Oloomi M. Short term load forecasting by using neural network structure. In: 2009 6th Int conf electr eng comput telecommun inf technol; 2009. p. 240-3. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5137001.
34. Chen C, Cook DJ. Behavior-based home energy prediction. In: 2012 Eighth int conf intell environ; 2012. p. 57-63. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6258503.
35. Bao K, Allerding F, Schmeck H. User behavior prediction for energy management in smart homes. In: 2011 Eighth int conf fuzzy syst knowl discov; 2011. p. 1335-9. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6019758.
36. Basu K., Hawarah L., Arghira N., Joumaa H., Ploix S. A prediction system for home appliance usage. Energy Build 2013, 67:668-679. . 10.1016/j.enbuild.2013.02.008.
37. Hooshmand R.-A., Amooshahi H., Parastegari M. A hybrid intelligent algorithm based short-term load forecasting approach. Int J Electr Power Energy Syst 2013, 45(1):313-324. . 10.1016/j.ijepes.2012.09.002.
38. Kibriya AM, Frank E. An empirical comparison of exact nearest neighbour algorithms; 2007. doi:10.1007/978-3-540-74976-9_16.
39. The Vienna University of Technology (Institute of Energy Systems and Electrical Drives), research project. "ADRES-CONCEPT"; 2012. http://www.ea.tuwien.ac.at/projekte/adres_concept/.
40. Widén J., Wäckelgård E. A high-resolution stochastic model of domestic activity patterns and electricity demand. Appl Energy 2010, 87(6):1880-1892. . 10.1016/j.apenergy.2009.11.006.
41. Richardson I, Thomson M, Infield D, Clifford C. Domestic electricity use: a high-resolution energy demand model. Energy Build. http://www.sciencedirect.com/science/article/pii/S0378778810001854.