Multi-substation control central load area forecasting by using HP-filter and double neural networks (HP-DNNs)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

doi:10.1016/j.ijepes.2012.08.002

Cited by 22 publications

(8 citation statements)

References 19 publications

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“…The co mprehensive list was developed fro m the literature [16][17][18][19][20][23][24][25][26][29][30][31][32][33][34]. The time period of data collection was January 1991 to December 2012.…”

Section: Data Set and Pre-processingmentioning

confidence: 99%

“…Several research studies have been conducted over the last decades to explore this comp lex problem of monthly electricity demand forecasting by means of mult ivariate t ime series analysis [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]7,[33][34][35]. Several studies have assumed that the input and output series are stationary and applied statistical models [16,36,17,[21][22][23]25,30,7].…”

Section: Introductionmentioning

confidence: 99%

“…When one or more of the input and output series are have nonstationary characteristic, it is necessary to consider the sophisticated models which are capable of describing the nonlinear input and output series. In order to solve nonlinear time series problems, most research studies applied auto-regressive integrated moving average model [17,20] and artificial neural network model-based approaches [18,19,24,26,28,29,[31][32][33][34]. The accuracy of the resulting models has ranged fro m 1.42% [34] to 10.98% [18] in terms of mean absolute percentage error.…”

Section: Introductionmentioning

confidence: 99%

“…In order to solve nonlinear time series problems, most research studies applied auto-regressive integrated moving average model [17,20] and artificial neural network model-based approaches [18,19,24,26,28,29,[31][32][33][34]. The accuracy of the resulting models has ranged fro m 1.42% [34] to 10.98% [18] in terms of mean absolute percentage error. However, it is difficult to say whether these models have been sufficiently validated because the evaluation periods of the most research studies were within about ten years [18][19][20]29,[31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…The accuracy of the resulting models has ranged fro m 1.42% [34] to 10.98% [18] in terms of mean absolute percentage error. However, it is difficult to say whether these models have been sufficiently validated because the evaluation periods of the most research studies were within about ten years [18][19][20]29,[31][32][33][34]. In addition, most of the previous studies have assumed that certain inputs among various weather variables and social variables have impact on the electricity demand series and fed these inputs to develop their models.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Short-term Electricity Demand in Residential Sector Based on Support Vector Regression and Fuzzy-rough Feature Selection with Particle Swarm Optimization

Son

Kim

2015

Procedia Engineering

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The aim of this study is to provide a precise model for one-month-ahead forecast of electricity demand in residential sector. In this study, a total of 20 influential variables are taken into account including monthly electricity consumption, 14 weather variables, and 5 social variables. Based on support vector regression and fuzzy-rough feature selection with particle swarm optimization algorithms, the proposed method established a model with variables that relate to the forecast without ignoring some of these variables one may inevitably lead to forecasting errors. The proposed forecasting model was validated using historical data from South Korea. Its time period was from January 1991 to December 2012. The first 240 months were used for training and the remaining 24 for testing. The performance was evaluated using M APE, M AE, RM SE, M BE, and UPA values. Furthermore, it was compared with that obtained from the artificial neural network, auto-regressive integrated moving average, multiple linear regression models, and the methods proposed in the previous studies, and found superior for every performance measure considered in this study. The proposed method has an advantage over the previous methods because it automatically determines appropriate and necessary variables for a reliable forecast. It is expected that the proposed model can contribute to more accurate forecasting of short-term electricity demand in residential sector. The ability to accurately forecast short -term electricity demand can assist power system operators and market participants in ensuring sustainable electricity planning decisions and secure electricity supply to the consumers.

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