A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

被引:338
作者
Hernandez, Luis [1 ]
Baladron, Carlos [2 ]
Aguiar, Javier M. [2 ]
Carro, Belen [2 ]
Sanchez-Esguevillas, Antonio J. [2 ]
Lloret, Jaime [3 ]
Massana, Joaquim [4 ]
机构
[1] Ctr Desarrollo Energias Renovables CEDER, Ctr Invest Energet Medioambientales & Tecnol CIEM, Lubia 42290, Soria, Spain
[2] Univ Valladolid, ETSI Telecomunicac, E-47011 Valladolid, Spain
[3] Univ Politecn Valencia, Dept Comunicac, Valencia 46022, Spain
[4] Univ Girona, EXiT Res Grp, Inst Informat & Applicat, Girona 17071, Spain
关键词
Electric demand forecasting; short-term load forecasting; smart grid; microgrid; smart building; ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR REGRESSION; ENERGY MANAGEMENT; SYSTEM ARCHITECTURE; MULTIAGENT SYSTEM; LOAD DEMAND; TERM; CLASSIFICATION; MODEL; SVR;
D O I
10.1109/SURV.2014.032014.00094
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the '70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids.
引用
收藏
页码:1460 / 1495
页数:36
相关论文
共 155 条
[1]   AN ACCURATE MODEL FOR SHORT-TERM LOAD FORECASTING [J].
ABOUHUSSIEN, MS ;
KANDIL, MS ;
TANTAWY, MA ;
FARGHAL, SA .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1981, 100 (09) :4158-4165
[2]   Using pattern recognition to identify habitual behavior in residential electricity consumption [J].
Abreu, Joana M. ;
Pereira, Francisco Camara ;
Ferrao, Paulo .
ENERGY AND BUILDINGS, 2012, 49 :479-487
[3]   2 NEW ALGORITHMS FOR ONLINE MODELING AND FORECASTING OF THE LOAD DEMAND OF A MULTI-NODE POWER SYSTEM [J].
ABUELMAGD, MA ;
SINHA, NK .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1981, 100 (07) :3246-3253
[4]   Cascaded artificial neural networks for short-term load forecasting [J].
AlFuhaid, AS ;
ElSayed, MA ;
Mahmoud, MS .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1997, 12 (04) :1524-1529
[5]  
Alvarez E, 2009, U POW ENG C UPEC P 4, P1
[6]   Short-term bus load forecasting of power systems by a new hybrid method [J].
Amjady, Nima .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (01) :333-341
[7]   Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy [J].
Amjady, Nima ;
Keynia, Farshid ;
Zareipour, Hamidreza .
IEEE TRANSACTIONS ON SMART GRID, 2010, 1 (03) :286-294
[8]   DISTINCTIVE FEATURES, CATEGORICAL PERCEPTION, AND PROBABILITY-LEARNING - SOME APPLICATIONS OF A NEURAL MODEL [J].
ANDERSON, JA ;
SILVERSTEIN, JW ;
RITZ, SA ;
JONES, RS .
PSYCHOLOGICAL REVIEW, 1977, 84 (05) :413-451
[9]  
[Anonymous], 1986, Parallel distributed processing
[10]  
[Anonymous], P 8 POW SYST COMP C