Sage Journals: Discover world-class research

Abstract

Smart grid has recently become a common system in many countries, and smart meter is one of the essential components of this system. The consumption data gathered from smart meters allow electricity companies to better understand electricity usage in the future and monitor electricity supply more efficiently.

An electricity consumption forecasting model established on the framework of support vector regression (SVR) was proposed in this study. Given that various factors affected consumption patterns, apart from historical data, weather variations and features of a particular time were also considered in this study. Based on this information, the stepwise regression analysis was applied for feature selection. However, the accuracy of the SVR model is largely dependent on the selection of the model parameters. The particle swarm optimization (PSO) algorithm was proposed to determine the optimal values of parameter that improves the accuracy and efficiency of the SVR model.

The experimental results show that this method can provide electricity forecasts with 0.70% and 2.55% of mean absolute percentage error(MAPE) for 1 and 24 hours ahead, respectively. This result indicates promising performance in forecasting accuracy. This model is also computationally efficient and can be applied to make predictions within 1 second.

Keywords

Smart grids electricity load forecasting support vector regression particle swarm optimization

Get full access to this article

View all access options for this article.

References

Taiwan Power Company, http://www.taipower.com.tw.

Taiwan Central Weather Bureau, http://www.cwb.gov.tw/V7/index.htm.

Tokyo Electric Power Company, http://www.tepco.co.jp/en/index-e.html.

Andrew

C.C.

, Comparing neural networks and regression models for ozone forecasting, Journal of the Air & Waste Management Association 47(6) (1997), 653-663, DOI: 10.1080/10473289.1997.10463925.

Basak

, Pal

and Patranabis

D.C.

, Support vector regression, Neural Information Processing - Letters and Reviews 11(10) (2007).

Carlisle

and Dozier

, An off-the-shelf PSO, Proceedings of the Workshop on Particle Swarm Optimization, (2001).

Cherkassky

and Ma

, Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks 17(1) (2004), 113-126.

Clerc

, The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation 3 (1999), 1951-1957.

Duan

, Keerthi

and Poo

, Evaluation of simple performance measures for tuning SVM hyperparameters, Technical Report, Department of Mechanical Engineering, National University of Singapore, 2001.

10.

Eberhart

R.C.

and Shi

, Tracking and optimizing dynamic systems with particle swarms, Proceedings of the 2001 Congress on Evolutionary Computation 1 (2001), 94-100.

11.

Fan

, Chen

and Lee

W.J.

, Machine learning based switching model for electricity load forecasting, Energy Conversion and Management 49 (2008), 1331-1344.

12.

Fok

C.Y.

and Vai

M.I.

, Very short term load forecasting for macau power system, Intelligent Computing Technology 7389 (2012), 538-546.

13.

Hassan

, Cohanim

and de Weck

, A comparison of particle swarm optimization and the genetic algorithm, 2004.

14.

Humeau

, Wijaya

T.K.

, Vasirani

and Aberer

, Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households, Sustainable Internet and ICT for Sustainability (SustainIT), (2013).

15.

Kandil

, Wamkeue

, Saad

and Georges

, An efficient approach for short term load forecasting using artificial neural networks, Electrical Power and Energy Systems 28 (2006), 525-530.

16.

Liang

Y.Z.

, Xu

Q.S.

, Li

H.D.

and Cao

D.S.

, Support Vector Machines and Their Application in Chemistry and Biotechnology, CRC Press, 2011.

17.

Liu

, Babushkin

and Afshari

, Short-term forecasting of temperature driven electricity load using time series and neural network model, Journal of Clean Energy Technologies 2(4) (2014).

18.

Meng

, Shang

and Niu

, Monthly electric energy consumption forecasting using multiwindow moving average and hybrid growth models, Journal of Applied Mathematics 2014 (2014), 7, Article ID 243171.

19.

Shi

and Eberhart

R.C.

, Empirical study of particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation 3 (1999).

20.

Sood

, Koprinska

and Agelidis

V.G.

, Electricity load forecasting based on autocorrelation analysis, The 2010 International Joint Conference on Neural Networks (IJCNN), (2010).

21.

Vapnik

and Cortes

, Support-vector networks, Machine Learning 20(3) (1995), 273.

22.

Zhang

G.P.

, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50 (2003), 159-175.

Electricity load forecasting in a smart grid system

Abstract

Keywords

Get full access to this article

References