A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network
By Feng Yu, Xiaozhong Xu. 2014
521 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/83d0bbb6a8c26e61896181d1c899675b25bcae00
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
- Zheng Wang
- Dipti Srinivasan
- Qinghua Hu
- Saifur Rahman
- Manisa Pipattanasomporn
- Yun Wang
- Mengmeng Cai
- Suryanarayana Doolla
In Electrical and Electronic Engineering
- Zheng Wang
- Dipti Srinivasan
- Qinghua Hu
- Saifur Rahman
- Manisa Pipattanasomporn
- Yun Wang
- Mengmeng Cai
- Suryanarayana Doolla
Citations in the corpus
- 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].”
- Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data (2019, methodology): “BP neural network uses error gradient descent algorithm to minimize the total error E, which is defined as E = ∑N i=1(y o i − y i )2 , where N is the length of the training data, y i and y r i are the output of network and the real data of the ith sample, respectively [25].”