Data-driven Approaches Predict Hourly Electricity Demand Profiles at Industry and City-level
Author:
Affiliation:
1. Marketing Service Center State Grid Fujian,Fuzhou,China
2. Electric Power Co.Ltd State Grid Fujian,Fuzhou,China
3. College of Energy Xiamen University,Xiamen,China
Funder
Fundamental Research Funds for the Central Universities
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10135545/10135141/10135675.pdf?arnumber=10135675
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1. Long Short-Term Memory
2. Regression Model-Based Short-Term Load Forecasting for University Campus Load
3. Framewise phoneme classification with bidirectional LSTM and other neural network architectures
4. Day-Ahead Short-Term Load Forecasting for Holidays Based on Modification of Similar Days’ Load Profiles
5. Short-Term Load Forecasting of Smart Grid Based on Load Spatial-Temporal Distribution
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