Short-term load forecasting using a two-stage sarimax model

Tarsitano, Agostino; Amerise, Ilaria Lucrezia

doi:10.1016/j.energy.2017.05.126

Cited by 80 publications

(33 citation statements)

References 16 publications

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“…There are abundant studies proposing ways to improve electric load forecasting accuracy in the literature, which are classified into two categories: statistical models and intelligent models. Statistical models, including the ARIMA model [2][3][4], regression model [5][6][7], exponential smoothing model [8][9][10], Kalman filtering model [11,12], and Bayesian estimation models [13,14], etc., are well known. These statistical models are superior choices to deal with simple linear electric load patterns, such as their increasing tendency.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Hong

Fan²

2019

Energies

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For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

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Section: Introductionmentioning

confidence: 99%

Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Hong

Fan²

2019

Energies

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“…Then, the KKT conditions are applied to the regression, Equation (7) would yield the dual Lagrangian by substituting Equations (9)-(12) into Equation (8). The dual Lagrangian, Equation (13), can be computed.…”

Section: Support Vector Regression Modelmentioning

confidence: 99%

“…These three items are separately modeled by the employed SVR-QGA model, and the final forecasting values would be computed as A + B − C. Therefore, this paper proposes an effective electricity consumption forecasting model, namely EMD-SVR-QGA model, with these three items to forecast the electricity consumption of a university dormitory, China. The forecasting results indicate that the proposed model outperforms other compared models.Mathematics 2019, 7, 1188 2 of 23 various statistical models that contain the ARIMA models [6][7][8], regression models [9][10][11], exponential smoothing models [12,13], Kalman filtering models [14,15], Bayesian estimation models [16,17], and so on. However, the inherent shortcomings of these statistical models are that they are only defined to deal with the linear relationships among the electricity consumption and other influenced factors mentioned above, eventually, only receiving unsatisfied forecasting results [18].Along with advanced nonlinear computing ability, the AI models have been mature diffusely explored to improve the forecasting accuracy of electricity consumption since the 1980s, such as artificial neural networks (ANNs) [18][19][20][21][22], expert system models [23][24][25][26], and fuzzy inference methodologies [27][28][29][30].…”

mentioning

confidence: 99%

“…Mathematics 2019, 7, 1188 2 of 23 various statistical models that contain the ARIMA models [6][7][8], regression models [9][10][11], exponential smoothing models [12,13], Kalman filtering models [14,15], Bayesian estimation models [16,17], and so on. However, the inherent shortcomings of these statistical models are that they are only defined to deal with the linear relationships among the electricity consumption and other influenced factors mentioned above, eventually, only receiving unsatisfied forecasting results [18].…”

mentioning

confidence: 99%

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Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory

Zhou

Xia

et al. 2019

Mathematics

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In the context of the nationwide call for “energy savings” in China, it is desirable to establish a more accurate forecasting model to manage the electricity consumption from the university dormitory, to provide a suitable management approach, and eventually, to achieve the “green campus” policy. This paper applies the empirical mode decomposition (EMD) method and the quantum genetic algorithm (QGA) hybridizing with the support vector regression (SVR) model to forecast the daily electricity consumption. Among the decomposed intrinsic mode functions (IMFs), define three meaningful items: item A contains the terms but the residual term; item B contains the terms but without the top two IMFs (with high randomness); and item C contains the terms without the first two IMFs and the residual term, where the first two terms imply the first two high-frequency part of the electricity consumption data, and the residual term is the low-frequency part. These three items are separately modeled by the employed SVR-QGA model, and the final forecasting values would be computed as A + B − C. Therefore, this paper proposes an effective electricity consumption forecasting model, namely EMD-SVR-QGA model, with these three items to forecast the electricity consumption of a university dormitory, China. The forecasting results indicate that the proposed model outperforms other compared models.

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“…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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Short-term load forecasting using a two-stage sarimax model

Cited by 80 publications

References 16 publications

Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory

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

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