Pattern-based local linear regression models for short-term load forecasting

By Grzegorz Dudek. 2016

316 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8038017306c4ad88d40d338f96906deaf8ce71c2

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

1. Ling Zhao
2. Chao Zhang
3. Tao Lin
4. Ljupco Kocarev
5. Min Deng
6. Pu Wang
7. Haifeng Li
8. Huiting Zheng

In Management Science and Operations Research

1. Ling Zhao
2. Chao Zhang
3. Tao Lin
4. Ljupco Kocarev
5. Min Deng
6. Pu Wang
7. Haifeng Li
8. Huiting Zheng

Citations in the corpus

• Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure (2015, methodology): “In recent research of [24] the lasso approach is applied to short-term electricity load forecasting.”
• Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, background): “[18,19], or it may be applied to smaller scale such as local areas in a country [20], a region [21,22], a city [23], a campus [24,25], in microgrid environments [26] or a household [27,28], which is one of the fundamental topics in smart grids.”
• Forecasting Indonesia's electricity load through 2030 and peak demand reductions from appliance and lighting efficiency (2019, unknown): “Some focus on forecasts in the short (day) ormedium (week or month) term, mostly to address individual aspects of hourly load projections such as economic power generation, system security, or renewable integration (Khotanzad, Rohani, & Maratukulam, 1998; Santos, Martins, & Pires, 2007; Saini, 2008;”
• Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation (2017, methodology): “Traditional methods are based on mathematical models, including multiple linear regression [1], stochastic time series [2], exponential smoothing [3], and knowledge-based methods [4].”
• T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2018, methodology): “regression model [27], [28], and the Kalman filter model are common methods.”