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

被引：60

作者：

El-Baz, Wessam ^{[1
]}

Tzscheutschler, Peter ^{[1
]}

机构：

[1] Tech Univ Munich, Inst Energy Econ & Applicat Technol, D-80333 Munich, Germany

来源：

APPLIED ENERGY | 2015年 / 147卷

关键词：

Smart home; Energy prediction; Energy planning; Electrical load; User behavior; Demand side management; ARTIFICIAL NEURAL-NETWORK; CONSUMPTION; MODEL; FORECAST; BEHAVIOR;

D O I：

10.1016/j.apenergy.2015.01.122

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Energy management system (EMS) within buildings has always been one of the main approaches for an automated demand side management (DSM). These energy management systems are supposed to increase load flexibility to fit more the generation from renewable energies and micro co-generation devices. For EMS to operate efficiently, it must learn ahead about the available supply and demand so that it can work on supply-demand matching and minimizing the imports from the grid and running costs. This article presents a simple efficient day-ahead electrical load prediction approach for any EMS. In comparison to other approaches, the presented algorithm was designed to be apart of any generic EMS and it does not require to be associated with a prepared statistical or historical databases, or even to get connected to any kinds of sensors. The proposed algorithm was tested over the data of 25 households in Austria and the results have shown an error range that goes down to 8.2% as an initial prediction. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：10 / 19

页数：10

共 41 条

[1] A review on applications of ANN and SVM for building electrical energy consumption forecasting [J].

Ahmad, A. S. ;

Hassan, M. Y. ;

Abdullah, M. P. ;

Rahman, H. A. ;

Hussin, F. ;

Abdullah, H. ;

Saidur, R. .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 33 :102-109

[2] Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data [J].

Akdemir, Bayram ;

Cetinkaya, Nurettin .

2011 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY ENGINEERING (ICAEE), 2012, 14 :794-799

[3] Long term forecasting of hourly electricity consumption in local areas in Denmark [J].

Andersen, F. M. ;

Larsen, H. V. ;

Gaardestrup, R. B. .

APPLIED ENERGY, 2013, 110 :147-162

[4] Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers [J].

Andersen, F. M. ;

Larsen, H. V. ;

Boomsma, T. K. .

ENERGY CONVERSION AND MANAGEMENT, 2013, 68 :244-252

[5] Domestic heat demand prediction using neural networks [J].

Bakker, Vincent ;

Molderink, Albert ;

Hurink, Johann L. ;

Smit, Gerard J. M. .

ICSENG 2008: INTERNATIONAL CONFERENCE ON SYSTEMS ENGINEERING, 2008, :189-194

[6]

Barbato A., 2012, 2012 IEEE International Energy Conference (ENERGYCON 2012), P701, DOI 10.1109/EnergyCon.2012.6348242

[7] Key energy management issues of setting market clearing price (MCP) in micro-grid scenario [J].

Basu, A. K. ;

Panigrahi, T. K. ;

Chowdhury, S. ;

Chowdhury, S. P. ;

Chakraborty, N. ;

Sinha, A. ;

Song, Y. H. .

2007 42ND INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE, VOLS 1-3, 2007, :854-860

[8] A prediction system for home appliance usage [J].

Basu, Kaustav ;

Hawarah, Lamis ;

Arghira, Nicoleta ;

Joumaa, Hussein ;

Ploix, Stephane .

ENERGY AND BUILDINGS, 2013, 67 :668-679

[9] Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output [J].

Cassola, Federico ;

Burlando, Massimiliano .

APPLIED ENERGY, 2012, 99 :154-166

[10] PV self-consumption optimization with storage and Active DSM for the residential sector [J].

Castillo-Cagigal, M. ;

Caamano-Martin, E. ;

Matallanas, E. ;

Masa-Bote, D. ;

Gutierrez, A. ;

Monasterio-Huelin, F. ;

Jimenez-Leube, J. .

SOLAR ENERGY, 2011, 85 (09) :2338-2348

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