Short-Term Load Forecasting With Exponentially Weighted Methods
By James W. Taylor. IEEE Transactions on Power Systems, 2012
298 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/f68dd61056ff3aad1707ff183f8e910ca9dcd255
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Grzegorz Dudek
- Siddharth Arora
- Kafait Ullah
- M. Imran
- Mohammed A. Alghassab
- Aneeque A. Mir
- Yuehong Lu
- Zafar Ali Khan
In Environmental Engineering
- Grzegorz Dudek
- Siddharth Arora
- Heng Huang
- David Yanshi Wang
- Robert L. Szabados
- Weijen Lee
- Franklin L. Quilumba
- Kafait Ullah
In Global and Planetary Change
- Grzegorz Dudek
- Siddharth Arora
- Heng Huang
- David Yanshi Wang
- Robert L. Szabados
- Weijen Lee
- Franklin L. Quilumba
- Kafait Ullah
Citations in the corpus
- Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations (2014, background): “It is a basic task to support the decision of electricity procurement [24], [25] and can facilitate renewable penetrations [26], [27].”
- Pattern-based local linear regression models for short-term load forecasting (2016, methodology): “3 The recently developed exponentially weighted methods in application to STLF are presented in [3].”
- Electricity load demand forecasting using exponential smoothing methods (2013, background): “These are the key features in forecasting Exponential Smoothing Methods electricity demand load time series [2].”
- Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities (2015, methodology): “Recently, machine learning techniques [12], fuzzy logic [7], and exponentially weighted [13] approaches have been used for load forecasting or classification and performed relatively well.”
- A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, methodology): “These methods have also been compared with other methods like discount weighted regressions, cubic splines, and a singular value decomposition [85].”
- Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods (2013, methodology): “Numerous statistical methods have been proposed for shortterm load forecasting [2], [3], whereby the lead times under consideration vary from a few minutes up to a day ahead.”