Forecasting Residential Electricity Based on FOAGMNN

Dong Xiao Niu; Ting Ting Chen; Peng Wang; Yan Chao Chen

doi:10.4028/www.scientific.net/AMR.860-863.2513

Paper Titles

Modeling and Simulation of Compressed Air Energy Storage (CAES) System for Electromechanical Transient Analysis of Power System
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Load Scheduling Strategies Based on Dynamic Price in Smart Grid
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High Precision Time Synchronization Algorithm for Smart Grid Based on Adaptive Filtering with Modified Particle Swarm Optimization
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Research on Key Indicators of Operational Warning System of Grid Enterprises Based on BSC-ISM Method
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Forecasting Residential Electricity Based on FOAGMNN
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Data Intergration of Power Information System Based on Ontology
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The Research of BP-PSO Optimization Method in Electric Power Market Analysis and Forecast
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Wind Power Output Prediction with BP Neural Network Combining Genetic Algorithms
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Reconfiguration of the Shipboard Power System Based on Particle Swarm Optimization with Non-Clique Topology
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Forecasting Residential Electricity Based on FOAGMNN

Abstract:

This paper puts forward a residential electricity forecasting method based on FOAGMNN. Correlation analysis was adopted to select the key influencing factors of residential electricity forecasting. Finally, annual disposable income, population, households, per capita floor space, preceding electricity consumption are choosed as the key influencing factors. Through simulation example using the data of Hangzhou residential electricity consumption from 2000 to 2011, the results showed that the proposed model outperformed the other models and is suitable for residential electricity prediction.