Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process

Hu, Yusha; Li, Jigeng; Hong, Mengna; Ren, Jingzheng; Lin, Ruojue; Liu, Yue; Liu, Mengru; Man, Yi

doi:10.1016/j.energy.2018.12.208

Cited by 156 publications

(43 citation statements)

References 50 publications

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“…For previous examples of GAs used in electric power systems, see [19] and [20]. The proposed algorithm analyses the influence of the input variables to the predicted electric load.…”

Section: The Proposed Methodologymentioning

confidence: 99%

Procedure for creating custom multiple linear regression based short term load forecasting models by using genetic algorithm optimization

2021

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This paper presents a novel procedure for short-term load forecasting (STLF) in distribution management systems (DMS). The load is forecasted for feeders that can be of a primarily residential, commercial, industrial or combined type. Each feeder has various amounts of distributed energy resources (DER) installed, which accounts for multiple different load patterns. Hence, the DMS cannot use a single STLF model for all forecasts. The proposed procedure addresses the specificity of each particular feeder type, by creating customized STLF models. It uses a genetic algorithm (GA) to select the best inputs for different multiple linear regression (MLR) models. The GA chooses variables from a dataset constructed using load and temperature measurements. The dataset is extended by adding nonlinear transformations and mutual interaction effects of the measurements, as well as calendar variables. This extension enables for the modelling of nonlinear influences and extracts the nonlinearity to the domain of input variables. The models' performance is assessed by the mean absolute percentage error (MAPE). The proposed procedure is applied to a set of measurements collected from a US electric power utility, which operates in the city of Burbank, CA. The obtained MLR model is compared with a previously proposed naïve benchmark, and a special comparison model, developed by correlation analysis. The proposed method is extendable to suit DMS systems with different types of electricity consumers.

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“…For previous examples of GAs used in electric power systems, see [19] and [20]. The proposed algorithm analyses the influence of the input variables to the predicted electric load.…”

Section: The Proposed Methodologymentioning

confidence: 99%

Procedure for creating custom multiple linear regression based short term load forecasting models by using genetic algorithm optimization

2021

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“…erefore, the LS-SVM is used to predict the intermediate and low frequency components. e method of BPNN and LS-SVM are very mature and have been widely used in power system load forecasting [11,23,25], and will not be described here.…”

Section: Various Frequency Component Predictionmentioning

confidence: 99%

“…While the nonparametric model method overcomes these weaknesses, and it can adapt to electrical load forecasting with nonlinearity, uncertainty and time-varying nature [6]. e methods based on nonparametric models mainly include wavelet analysis method [7,8], grey model method [9], support vector machine (SVM) [10] and artificial neural network method [11][12][13], etc. ese methods could consider the nonlinearity of the electrical load, and the law of the electrical load could be found through fitting and approximating of the original data.…”

Section: Introductionmentioning

confidence: 99%

“…erefore, neural networks have a wide range of applications in the field of power load forecasting. For example, literature [11] proposed a short term electrical load forecasting model based on the hybrid GA-PSO-BPNN algorithm. In literature [12], considering the influencing factors such as temperature, relative humidity, date type, and historical load, a new shortterm load prediction method based on kernel function extreme learning machine model is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, in order to overcome the shortcomings of BPNN, many scholars have proposed di erent methods to optimize the initial connection weight value and thresholds of traditional BPNN. For example, the literature [11] uses genetic algorithms and particle swarm optimization (PSO) to optimize BPNN parameters. Both of the convergence speed and prediction accuracy of optimized model are superior to the traditional BP neural network model [26].…”

Section: Introductionmentioning

confidence: 99%

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Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

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Complexity

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Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

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Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

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2021

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Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process

Cited by 156 publications

References 50 publications

Procedure for creating custom multiple linear regression based short term load forecasting models by using genetic algorithm optimization

Procedure for creating custom multiple linear regression based short term load forecasting models by using genetic algorithm optimization

Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

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