Research of Load Forecasting Based on General Regression Neural Network

Cheng Ming Wu; Wei Wei Yao; Wen Jing Dong

doi:10.4028/www.scientific.net/AMR.732-733.926

Paper Titles

Power System Stability Study with Empirical Mode Decomposition
p.905

Real-Time Dispatching Operation Risk Assessment Based on Fault Tree Theory
p.909

Reliability Evaluation of HVDC System Using FTA-FD Method Considering Different Capacities of AC Filters
p.915

Research and Application on the Reactive Power Optimization of the Regional Grid Planning
p.920

Research of Load Forecasting Based on General Regression Neural Network
p.926

Research on the Transformer Condition Assessment Based on the Entropy Weight Method
p.930

Simulation Analysis for the Impact of Voltage Distribution of Distribution System with Distributed Generation
p.936

State Estimation of Equivalent Current Measurement Transformation Based on Generalized Tellegen's Theorem
p.941

Study on Electric Energy Metering Program under the Impact of Distorted Waveform
p.948

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 732-733Research of Load Forecasting Based on General...

Research of Load Forecasting Based on General Regression Neural Network

Abstract:

Load forecasting technology is an important guarantee of the safe and steady operation in power system. Based on the analyzing and designing the network structure of GRNN, this paper sets an appropriate smoothing parameter and proposes a strategy for load forecasting under considering the weather factors. A short-term load forecasting model with the factors of temperature, humidity, wind speed, barometric pressure and rainfall is established. And the model can achieve the expected result after a complete test. Finally, comparing with the forecast model based on BP neural network, GRNN method shows the great superiority in power load forecasting.