Practical experiences with an adaptive neural network short-term load forecasting system

@inproceedings{Mohammed1995PracticalEW,
  title={Practical experiences with an adaptive neural network short-term load forecasting system},
  author={O. A. Mohammed and Daniel C. Park and Rubina Merchant and Thuy-Linh Dinh and Chong-Sze Tong and Ahmad Azeem and Joumana Farah and Chris Drake},
  year={1995}
}
An adaptive neural network based short-term electric load forecasting system is presented. The system is developed and implemented for Florida Power and Light Company (FPL). Practical experiences with the system are discussed. The system accounts for seasonal and daily characteristics, as well as abnormal conditions such as cold fronts, heat waves, holidays and other conditions. It is capable of forecasting load with a lead time of one hour to seven days. The adaptive mechanism is used to train… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 68 CITATIONS

A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

  • IEEE Communications Surveys & Tutorials
  • 2014
VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

A Hierarchical Self-Organizing Map Model in Short-Term Load Forecasting

  • Journal of Intelligent and Robotic Systems
  • 2001
VIEW 5 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

1995
2019

CITATION STATISTICS

  • 4 Highly Influenced Citations