A seasonal hybrid procedure for electricity demand forecasting in China

Zhu, Suling; Wang, Jianzhou; Zhao, Weigang; Wang, Jujie

doi:10.1016/j.apenergy.2011.05.005

Cited by 89 publications

(48 citation statements)

References 32 publications

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“…Although several different forecasting methods are used for prediction of electricity demand, none of them is superior in all cases. Some of these techniques used to forecast electricity demand of countries are the time series model (Saab et al, 2001;Sa'ad, 2009;Dilaver and Hunt, 2011;Boran, 2014;Efendi et al, 2014), artificial neural networks (ANNs) model (Hamzacebi and Kutay, 2004;Hamzacebi, 2007;Azadeh et al, 2008;Cunkas and Altun, 2010;Panklib et al, 2015) , regression and econometric model (Mohamed and Bodger, 2005;Al-Shobaki and Mohsen, 2008;Meng and Niu, 2011;Bildirici et al, 2012;Bianco et al, 2013), neuro-fuzyy model (Demirel et al, 2010;Chang et al, 2011), heuristic optimization method (El-Telbany and ElKarmi, 2008;Cunkas and Taskiran, 2011;Zhu et al, 2011), and support vector regression model (SVR) (De Felice et al, 2015;Jain et al, 2014;Kaytez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The authors examined the advantage of forecasting with the SARIMA method. Zhu et al (2011) developed an improved hybrid model for electricity demand in China, which takes the advantages of moving average procedure, combined method, hybrid model and adaptive particle swarm optimization algorithm, known as MA-C-WH. Wang et al (2012) applied particle swarm optimization (PSO) optimal Fourier method, seasonal ARIMA model and combined models of PSO optimal Fourier method with seasonal ARIMA.…”

Section: Introductionmentioning

confidence: 99%

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Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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show abstract

“…The autonomous models are based 33 on the historical data of the electricity demand for forecasting the future demand while the conditional models build up the 34 relationship between the electricity demand and the other associated variables, and then forecast the future demand based on the 35 changes in the variables. Since the combination of the traditional models can utilize the advantages of individual models, the combinatorial hybrid 39 model has been used in [12] for electricity demand forecasting. This article has illustrated that the combination of the two main 40 techniques i.e., moving average procedure and adaptive particle swarm optimization algorithm is very effective for forecasting 41 electricity demand.…”

mentioning

confidence: 99%

A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables

2015

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Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using the proposed model are found to be closely matched with the actual electricity demands highlighting the fact that the prediction errors are well within the acceptable limits. Abstract: Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy 10 of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying 11 geographical conditions. This paper uses multicollinearity and backward elimination processes to select the most appropriate 12 variables and develop a multiple regression model for monthly forecasting of electricity demand. The former process is 13 employed to reduce the collinearity between the explanatory variables by excluding the predictor which has highly linear 14 relationship with the other independent variables in the dataset. In the next step, involving backward elimination regression 15 analysis, the variables with coefficients that have a low level of significance are removed. A case study has been reported in this 16paper by acquiring the data from the state of New South Wales, Australia. The data analyses have revealed that the climatic 17 variables such as temperature, humidity, and rainy days predominantly affect the electricity demand of the state of New South 18Wales. A regression model for monthly forecasting of the electricity demand is developed using the climatic variables that are 19 dominant. The model has been trained and validated using the time series data. The monthly forecasted demands obtained using 20 the proposed model are found to be closely matched with the actual electricity demands highlighting the fact tha...

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“…About the load forecasting issues, researchers have made good use of the characteristic to refine the model [16].…”

Section: Electricity Load Characteristicsmentioning

confidence: 99%

An Adaption of Pattern Sequence-based Electricity Load Forecasting with Match Filtering

Chu¹,

Jung²

2017

Journal of Korea Multimedia Society

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The Pattern Sequence-based Forecasting (PSF) is an approach to forecast the behavior of time series based on similar pattern sequences. The innovation of PSF method is to convert the load time series into a label sequence by clustering technique in order to lighten computational burden. However, it brings about a new problem in determining the number of clusters and it is subject to insufficient similar days occasionally. In this paper we proposed an adaption of the PSF method, which introduces a new clustering index to determine the number of clusters and imposes a threshold to solve the problem caused by insufficient similar days. Our experiments showed that the proposed method reduced the mean absolute percentage error (MAPE) about 15%, compared to the PSF method.

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A seasonal hybrid procedure for electricity demand forecasting in China

Cited by 89 publications

References 32 publications

Primary energy sources planning based on demand forecasting: The case of Turkey

Primary energy sources planning based on demand forecasting: The case of Turkey

A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables

An Adaption of Pattern Sequence-based Electricity Load Forecasting with Match Filtering

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