Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China

Wang, Yuanyuan; Wang, Jianzhou; Zhao, Ge; Dong, Yao

doi:10.1016/j.enpol.2012.05.026

Cited by 210 publications

(81 citation statements)

References 26 publications

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“…(12), and Eqs. (17) to (19), each error analysis indicator for each forecasting model was calculated (see Table 2). …”

Section: Forecasting Results and Error Analysismentioning

confidence: 99%

See 1 more Smart Citation

Yearly Electricity Consumption Forecasting using a Nonhomogeneous Exponential Model Optimized by PSO Algorithm

Xu¹,

Niu²,

Meng³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Yearly electricity consumption trends of most developing countries usually exhibit approximately exponential growth curves. An optimized nonhomogeneous exponential model (ONEM) is proposed as a method of forecasting electricity consumption by using trend extrapolation. The parameters of the nonhomogeneous exponential equation are obtained by using the inverse accumulated generating operation, discretizing the differential equation, minimizing the residual sum of squares (RSS), and accumulating the homogeneous exponential equation. Furthermore, to improve forecasting precision, particle swarm optimization (PSO) algorithm is used to optimize the equation parameters. To evaluate the forecasting performance for comparison, the said model and two other traditional methods are used to forecast the yearly electricity consumption of India. Empirical results show that this model is much better than traditional methods for each error analysis indicator.

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“…(12), and Eqs. (17) to (19), each error analysis indicator for each forecasting model was calculated (see Table 2). …”

Section: Forecasting Results and Error Analysismentioning

confidence: 99%

“…Other researchers concentrate on the change rule of the electricity consumption trend itself. They use the historical electricity consumption data to build trend simulation equations and obtain the forecasting results by trend extrapolation [15,16,17]. The forecasting model proposed in this paper belongs to the latter.…”

Section: Introductionmentioning

confidence: 99%

Yearly Electricity Consumption Forecasting using a Nonhomogeneous Exponential Model Optimized by PSO Algorithm

Xu¹,

Niu²,

Meng³

et al. 2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…An accurate prediction can help decision makers develop an optimal action program because electricity is hard to store. However, the low-voltage side of DSM is affected by various unstable factors, such as technical progress, holidays, policy, law, population growth, the natural and social environment and so on [3]. Therefore, developing new forecasting methods and improving the accuracy become imperative, no matter for very short-term, short-term, midterm or long-term load forecasts.…”

Section: Introductionmentioning

confidence: 99%

The Electrical Load Forecasting Base on an Optimal Selection Method of Multiple Models in DSM

Zheng

Zhang

2015

Int. J. Onl. Eng.

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Abstract-Electrical load forecasting plays a key role in energy scheduling and planning. It is a challenge to predict electric load accurately due to the versatility of electrical loads and the vast number of users in DSM of low-voltage side. Most of electrical load forecasting research focused on single model prediction or combination model prediction, which cannot get the optimal performance for some cases. Therefore, how to gather maximum optimal information from various different models is a key point in load forecasting and analysis. In this paper, an optimal selection method of multiple models for electrical load forecasting is studied. This method overcomes the shortcoming of unitary model, such as the instability and poor accuracy in some cases. To evaluate the forecast performance, a practical case is studied based on the intelligent electricity management system, which is presented by Wuhan University. It can be seen that the prediction error of the forecasting models can be calculated automatically and final optimum model can be obtained by optimum seeking software platform.

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“…Especially for urban water demand modeling, the ARIMA model has performed more accurately than time-series and multiple regression methods when forecasting demand based on climate variables [9]. ARIMA models are extensively used in time-series forecasting, especially in water and energy forecasting [10,11]. For instance, Praskievicz and Chang [12] used daily and monthly data from 2002 to 2007 to conduct a statistical analysis of seasonal water consumption in Seoul, South Korea.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

et al. 2015

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Abstract:The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient OPEN ACCESSSustainability 2015, 7 11051 technologies in households and reduction in water supply loss through investments on distribution infrastructure.

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Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China

Cited by 210 publications

References 26 publications

Yearly Electricity Consumption Forecasting using a Nonhomogeneous Exponential Model Optimized by PSO Algorithm

Yearly Electricity Consumption Forecasting using a Nonhomogeneous Exponential Model Optimized by PSO Algorithm

The Electrical Load Forecasting Base on an Optimal Selection Method of Multiple Models in DSM

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

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