A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm

By Hongze Li, Sen Guo, Chun-jie Li, Jingqi Sun. Knowledge-Based Systems, 2013

458 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/057c19fb706c491e6b03d26ca695280b7ac428f0

How researchers see this paper

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

In Artificial Intelligence

1. Antonio J. Sánchez-Esguevillas
2. Jaime Lloret
3. Belén Carro
4. Iqbal H. Sarker

In Electrical and Electronic Engineering

1. Antonio J. Sánchez-Esguevillas
2. Jaime Lloret
3. Belén Carro

In Management Science and Operations Research

1. Antonio J. Sánchez-Esguevillas
2. Jaime Lloret
3. Poom Kumam
4. Belén Carro

Citations in the corpus

• A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings (2014, methodology): “All these models are defined by the large amount of data needed for the learning phase (two years on average except [113] which requires 24 years because the one year forecasting horizon).”
• Fractional Neuro-Sequential ARFIMA-LSTM for Financial Market Forecasting (2020, methodology): “Generalized regression neural network (GRNN) is used for approximation of function [43].”