Short-term load forecasting using lifting scheme and ARIMA models
By Cheng-Ming Lee, Chia-Nan Ko. Expert systems with applications, 2011
393 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/7615a3c0efea50a1d9bd90111e55ebe9676a01f1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Chunhong Zhang
- .. Nahid-Al-Masood
- Oliver Sawodny
- Saifur Rahman
- Manisa Pipattanasomporn
- Jian Ma
- Shohana Rahman Deeba
- Grzegorz Dudek
In Electrical and Electronic Engineering
- Chunhong Zhang
- .. Nahid-Al-Masood
- Oliver Sawodny
- Saifur Rahman
- Manisa Pipattanasomporn
- Jian Ma
- Shohana Rahman Deeba
- Grzegorz Dudek
In Management Science and Operations Research
- Chunhong Zhang
- .. Nahid-Al-Masood
- Oliver Sawodny
- Saifur Rahman
- Manisa Pipattanasomporn
- Jian Ma
- Shohana Rahman Deeba
- Grzegorz Dudek
Citations in the corpus
- A Two-Stage Model Predictive Control Strategy for Economic Diesel-PV-Battery Island Microgrid Operation in Rural Areas (2016, methodology): “The implemented prediction method comprises of a a seasonal auto regression integrated moving average model (SARIMA) and exponential smoothing [40], [41].”
- Pattern-based local linear regression models for short-term load forecasting (2016, methodology): “Another time series decomposition method using lifting scheme (the second generation wavelet transform) was described in [5].”
- Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques (2019, methodology): “Most existing load forecasting models use non-DL techniques, ranging from the simplest multiple linear regression (MLR) [17,18], autoregressive integrated moving average (ARIMA) [19,20], support vector regression (SVR) [21,22] to the most sophisticated arti fi cial neural network (ANN) [23,24].”
- 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].”