Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model

Yuan, Chaoqing; Liu, Sifeng; Fang, Zhigeng

doi:10.1016/j.energy.2016.02.001

Cited by 325 publications

(161 citation statements)

References 19 publications

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“…Nagy et al [4] suggest a generalized additive tree ensemble approach to predict solar and wind power generation. Yuan et al [5] use autoregressive moving average model (ARIMA), grey model GM (1,1), and a hybrid…”

Section: Introductionmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

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Section: Introductionmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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“…A closer look at Figures 7,9,11,14,and 15 indicates that the hybrid model of MMPF (EKF-ARMA)+SVM performs much better than the rest since it has the lowest percentage error. The actual reason is because although the MMPF is indeed adaptive has a main disadvantage which is that in its initial structure is not able to handle non-linearities and seasonalities.…”

Section: Discussionmentioning

confidence: 99%

“…The problem of load forecasting has been studied extensively during recent decades. Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11][12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Pappas¹

2017

IJPER

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Abstract. Electric load forecasting is a process that has to be both fast and reliable. An accurate method of load forecasting plays the most crucial role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and operational problems connected to electrical energy production and distribution. In this study real data is used and the performance of three different techniques for adaptive electric load forecasting is evaluated. The first method is a combination of the well-established multimodel partitioning filter (MMPF) implementing extended Kalman filters (EKF) with Support Vector Machines (SVM), the second is the adaptive MMPF Kalman filters (KF) model and the third one is an artificial three layer feed-forward neural network (ANN). The results indicate that all three methods are reliable, however the combination of MMPF and SVM provides a more accurate load forecasting and at the same time identifies successfully both normal periodic behavior and any unusual activity of the electric grid.Keywords: Adaptive multimodel partitioning filter (MMPF), Support vector machines (SVM), artificial neural networks (ANN), forecasting, Kalman filters (KF), electricity demand load. IntroductionElectrical energy is considered as one of the most important factors that are closely related to both economic and social development. Accurate and fast load forecasting is related to a number of operational functions, such as system safety, power system real-time control, economic planning, grid's maintenance and also fuel planning. The problem of load forecasting has been studied extensively during recent decades. Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11-12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].The purpose of this study is not to introduce one more load forecasting criterion, but it focuses on applying three different methods to real electric load data and evaluating their performance.The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21]. The data used now is not subjected to any prior -offline manipulation in order to remove weekly and annual seasonality as was done in previous cases [5]. That's why in this case the MMPF implements a bank of extended Kalman filters (EKF) with ARMA models instead of simple Kalman filters (KF) with ARMA models in order to handle data's non-linearities. MMPF with EKF combined with genetic algorithms (GA) were successfully applied in prediction of epilepsy and in the evolution of stock values using biomedical and financial data respectively [22].The second method, presented analytically in [5], impl...

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“…Amongst which the most important one is the occurrence of "unit roots". For instance, as shown below, the autoregressive model of order 1 with lag 1 [19,20]:…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

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Abstract:Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan's energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector's demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

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Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model

Cited by 325 publications

References 19 publications

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

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