A Study on Exponential Smoothing Model for Load Forecasting

Ji, Peirong; Xiong, Dayuan; Wang, Peng; Chen, Juan

doi:10.1109/appeec.2012.6307555

Cited by 51 publications

(30 citation statements)

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“…They are also crucial for the purpose of a load forecasting in the power systems. As emphasized by Peirong and his colleagues [51], the accuracy of the model importantly depends on smoothing coefficients of the EF model. This study also demonstrates how to seek the best smoothing coefficients.…”

Section: Exponential Smoothing (Es)mentioning

confidence: 99%

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Hammad

Jereb

Rosi

et al. 2020

Logistics &Amp; Sustainable Transport

132

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Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

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Section: Exponential Smoothing (Es)mentioning

confidence: 99%

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Hammad

Jereb

Rosi

et al. 2020

Logistics &Amp; Sustainable Transport

132

View full text Add to dashboard Cite

show abstract

“…Therefore the accuracy depends on some coefficients. The authors in [29] studied exponential smoothing for load forecasting application using different coefficients. They used six different data sets collected from China to evaluate their model, and as it was assumed, they achieved a high range of MAPE for different coefficient values.…”

Section: ) Exponential Smoothingmentioning

confidence: 99%

On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach

et al. 2021

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Since electricity plays a crucial role in countries' industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This paper discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely "hourly load consumption of Malaysia" as well as "daily power electric consumption of Germany", are used to test and compare the presented models. To evaluate the tested models' performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this paper is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model's performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.

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“…It is often applied to shortterm and ultrashort-term power load forecasting and has high precision [27]. The prediction for the linear model of the exponential smoothing method is shown in…”

Section: The Exponential Smoothing Methodmentioning

confidence: 99%

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Fan

Duan

et al. 2018

Mathematical Problems in Engineering

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In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

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A Study on Exponential Smoothing Model for Load Forecasting

Cited by 51 publications

References 0 publications

Methods and Models for Electric Load Forecasting: A Comprehensive Review

Methods and Models for Electric Load Forecasting: A Comprehensive Review

On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

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