Abstract
The demand to electrical energy increases day by day. It is very important to reflect this increasing demand accurately to power plant planning. ANN technique can be effectively used in load forecasting. In this paper, ANN load forecasting is performed by using some nonlinear input parameters such as temperature, humidity, rain conditions. Real electrical data obtained for the national grid and meteorological parameters are used in the presented application.
Keywords
Artificial neural network
Power demand
Demand forecasting
Computer science
Electric power system
Electrical load
Nonlinear system
Power (physics)
Peak demand
Power network
Power grid
Peak load
Humidity
Smart grid
Energy demand
Estimation
Base load power plant
Automotive engineering
Engineering
Meteorology
Power consumption
Electricity
Artificial intelligence
Operations research
Voltage
Electrical engineering
Environmental economics
Subject Areas
Energy Load and Power Forecasting
·Electrical and Electronic Engineering ·Physical Sciences
Neural Networks and Applications
·Artificial Intelligence ·Physical Sciences
Advanced Algorithms and Applications
·Control and Systems Engineering ·Physical Sciences
OpenAlex SDG Match
SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).
Affordable and clean energy 67%
Loading…