Short-term load forecasting for the holidays using fuzzy linear regression method

By Kyung-Bin Song, Y. Baek, D. Hong, G. Jang. IEEE Transactions on Power Systems, 2005

475 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Ecological Modeling, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/ccaf40b4e3de7490a3e1a7ff8467ba9bf090935e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Shu Fan
2. Rob J Hyndman
3. Pu Li
4. Hui Zhang

In Ecological Modeling

1. Shu Fan
2. Rob J Hyndman
3. Pu Li
4. Hui Zhang

In Safety, Risk, Reliability and Quality

1. Shu Fan
2. Rob J Hyndman
3. Pu Li
4. Hui Zhang

Citations in the corpus

• Density Forecasting for Long-Term Peak Electricity Demand (2010, methodology): “These techniques can be classified into several groups: statistical models including linear regression and time series methods [2]–[6]; artificial intelligent approaches including artificial neural networks and fuzzy logic methods [7]–[12]; and other methods including machine learning, data mining t”
• Short-Term Load Forecasting With Deep Residual Networks (2018, methodology): “Some of the models used for STLF include linear or nonparametric regression [2], [3], support vector”
• A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network (2021, methodology): “These include multiple linear regression [4], [5], exponential smoothing [3] and autoregressive integrated moving normal (ARIMA) [6] algorithms.”
• A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network (2018, methodology): “Statistical methods are most frequently used in early literature, including multiple linear regression [10,11], exponential smoothing [12], and the autoregressive integrated moving average (ARIMA) [13].”
• Chance Constrained Programming for Optimal Power Flow Under Uncertainty (2011, methodology): “Since means a very large error of the load forecasting and is not common in short-term forecasting [16], it is realistic to assume for the random inputs considered in this study.”
• Short-term load forecasting based on an adaptive hybrid method (2006, background): “Previous works already show that different schemes should be developed for the regular days and anomalous days [18].”