Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

Yu, Jungwon; Kim, Sungshin

doi:10.5391/ijfis.2016.16.3.163

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…The LSTMNN has been widely used in many fields such as traffic forecasting [25], solar energy forecasting [26], stock price volatility prediction [27] and water table depth prediction [28]. These predictions all get good results [29].…”

Section: Introductionmentioning

confidence: 99%

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

Qin

Zhou

2019

Applied Sciences

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Accurate wind speed forecasting is of great significance for a reliable and secure power generation system. In order to improve forecasting accuracy, this paper introduces the LSTM neural network and proposes a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model. Moreover, the MIC is used to analyze the autocorrelation of wind speed series, and the predictable time of wind speed statistical forecasting method for direct multistep forecasting is taken as four hours. In the EEMD-FuzzyEn-LSTMNN model, the original wind speed series is firstly decomposed into a series of components by using EEMD. Then, the FuzzyEn is used to calculate the complexity of each component, and the components with similar FuzzyEn values are classified into one group. Finally, the LSTMNN model is used to forecast each subsequence after classification. The forecasting result of the original wind speed series is obtained by aggregating the forecasting result of each subsequence. Three forecasting cases under different terrain conditions were selected to validate the proposed model, and the BPNN model, the SVM model and the LSTMNN model were used for comparison. The experimental results show that the forecasting accuracy of the EEMD-FuzzyEn-LSTMNN model is much higher than that of the other three models.

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Section: Introductionmentioning

confidence: 99%

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

Qin

Zhou

2019

Applied Sciences

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“…Ivakhnenko first proposed the GMDH algorithm in 1968, which is a self-organizing modeling technique [32][33][34]. In the GMDH method, as shown in Figure 1, complex and nonlinear modeling is performed by the GMDH network where polynomial neurons are hierarchically connected in a forward direction.…”

Section: Group Methods Of Data Handlingmentioning

confidence: 99%

A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting

Park

Kim

2018

Energies

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Electric load forecasting is indispensable for the effective planning and operation of power systems. Various decisions related to power systems depend on the future behavior of loads. In this paper, we propose a new input selection procedure, which combines the group method of data handling (GMDH) and bootstrap method for support vector regression based hourly load forecasting. To construct the GMDH network, a learning dataset is divided into training and test datasets by bootstrapping. After constructing GMDH networks several times, the inputs that appeared frequently in the input layers of the completed networks were selected as the significant inputs. Filter methods based on linear correlation and mutual information (MI) were employed as comparison methods, and the performance of hybrids of the filter methods and the proposed method were also confirmed. In total, five input selection methods were compared. To verify the performance of the proposed method, hourly load data from South Korea was used and the results of one-hour, one-day and one-week-ahead forecasts were investigated. The experimental results demonstrated that the proposed method has higher prediction accuracy compared with the filter methods. Among the five methods, a hybrid of an MI-based filter with the proposed method shows best prediction performance.

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“…In current approaches using traditional time-series methods or complex machine learning models, limitations, such as low model accuracy, poor generalization capability, and relatively high requirements of the quantity and quality of data, exist [22]. In comparison, the advantage of support vector machine regression lies in its ability to handle nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Carbon Emissions Level in China’s Logistics Industry Based on the PSO-SVR Model

Chen,

Pan,

Zhang

2024

Mathematics

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Adjusting the energy structure of various industries is crucial for achieving China’s carbon peak and carbon neutrality goals. Given the significant proportion of carbon emissions from the logistics industry in the tertiary sector, the research on predicting the carbon emissions of the logistics industry is of great significance for China to achieve its “Dual carbon” target. In this paper, the gray relational analysis (GRA) methodology is adopted to screen the influencing factors of carbon emissions in the logistics industry firstly. Then, the particle swarm optimization (PSO) algorithm was used to optimize the penalty coefficientand kernel function range parameter of the support vector regression (SVR) model (i.e. PSO- SVR model). The data from 2000 to 2021 regarding carbon emissions and related influencing factors in China’s logistics industry are analyzed, and the mean absolute percentage error (MAPE) of the PSO-SVR model is 0.82%, which shows that the proposed PSO-SVR model in this paper is effective. Finally, instructive suggestions are provided for China to achieve the “Dual Carbon” goal and upgrading of the logistics industry.

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Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

Cited by 8 publications

References 18 publications

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting

Prediction of Carbon Emissions Level in China’s Logistics Industry Based on the PSO-SVR Model

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