Credibility forecasting in short‐term load forecasting and its application-Reference-Cited by-同舟云学术

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Credibility forecasting in short‐term load forecasting and its application

Published:2015-10 Issue:13 Volume:9 Page:1564-1571
ISSN:1751-8695
Container-title:IET Generation, Transmission & Distribution
language:en
Short-container-title:IET Generation, Transmission & Distribution

Author:

Li Canbing¹,Li Yijing¹,Cao Yijia¹,Ma Jin²,Kuang Yonghong¹,Zhang Zhikun¹³,Li Lijuan¹,Wei Jing¹

Affiliation:

1. College of Electrical and Information EngineeringHunan UniversityChangsha410082Hunan ProvincePeople's Republic of China

2. School of Electrical and Information EngineeringUniversity of SydneySydneyNSW2006Australia

3. Tangshan Power Supply CompanyTangshan063009Hebei ProvincePeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2014.0729

Reference28 articles.

1. Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting

2. Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods

3. Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals

4. Smart Operation of Smart Grid: Risk-Limiting Dispatch

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1. Distribution locational marginal price computation based on active power loss and emissions in active distribution system using nucleolus theory;Evolutionary Intelligence;2022-03-09

2. Saturated load forecasting based on clustering and logistic iterative regression;Electric Power Systems Research;2022-01

3. Shapley value cooperative game theory-based locational marginal price computation for loss and emission reduction;Protection and Control of Modern Power Systems;2021-11-10

4. Study on environment-concerned short-term load forecasting model for wind power based on feature extraction and tree regression;Journal of Cleaner Production;2020-08

5. Deep Learning-Based Time-Varying Parameter Identification for System-Wide Load Modeling;IEEE Transactions on Smart Grid;2019-11

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