BP neural network with rough set for short term load forecasting

Authors:
Zhi Xiao;Shi-Jie Ye;Bo Zhong;Cai-Xin Sun
Affiliations:
College of Economics and Business Administration, Chongqing University, 400044 Chongqing, China;College of Economics and Business Administration, Chongqing University, 400044 Chongqing, China;College of Mathematics and Physics, Chongqing University, 400044 Chongqing, China;Key Laboratory of High Voltage Engineering and Electrical New Technology, Ministry of Education, Chongqing University, 400044 Chongqing, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 0
Cited 14

HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR FORECASTING TIME-SERIES DATA

Applied Artificial Intelligence
A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization

Expert Systems with Applications: An International Journal
Conventional regression versus artificial neural network in short-term load forecasting

SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Short-term Maharashtra state electrical power load prediction with special emphasis on seasonal changes using a novel focused time lagged recurrent neural network based on time delay neural network model

Expert Systems with Applications: An International Journal
A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction

Expert Systems with Applications: An International Journal
Early warning of enterprise decline in a life cycle using neural networks and rough set theory

Expert Systems with Applications: An International Journal
Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting

Expert Systems with Applications: An International Journal
Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm

Neurocomputing
Aggregating multiple classification results using Choquet integral for financial distress early warning

Expert Systems with Applications: An International Journal
The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory

Knowledge-Based Systems
Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side

Expert Systems with Applications: An International Journal
MCELCCh-FDP: Financial distress prediction with classifier ensembles based on firm life cycle and Choquet integral

Expert Systems with Applications: An International Journal
Modeling data uncertainty on electric load forecasting based on Type-2 fuzzy logic set theory

Engineering Applications of Artificial Intelligence
Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

Knowledge-Based Systems

Quantified Score

Hi-index	12.06

Visualization

Abstract

Precise Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors to develop the accuracy of predictions. Through attribute reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the time taken for training is decreased. Using load time series from a practical power system, we tested the performance of RSBP by comparing its predictions with that of BP network.