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Power Peak Load Forecasting Based on Deep Time Series Analysis Method
Ying Chang Hung,
Duen Ren Liu
資訊管理研究所
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Keyphrases
Time Series Analysis Method
100%
Power Peaks
100%
Deep Time
100%
Peak Load Forecasting
100%
Moving Average
80%
Peak Power
60%
Power Load
40%
Self-attention Mechanism
40%
GRU Model
40%
Transformer Mechanism
40%
Power Supply
20%
Comparative Analysis
20%
Knowledge Base
20%
Proposed Methodology
20%
Data Preprocessing
20%
Performance Prediction
20%
Prediction Method
20%
Temporal Change
20%
Forecasting Techniques
20%
Modeling Process
20%
Electricity Consumption
20%
Seasonal Pattern
20%
Autoregressive Integrated Moving Average (ARIMA)
20%
Forecasting Problem
20%
Existing Knowledge
20%
Seasonal Variation
20%
Power Load Forecasting
20%
Energy Consumption Prediction
20%
Network Transformers
20%
Self-attention Network
20%
Predictive Modeling
20%
Predictive Modeling Techniques
20%
Landscapes of Power
20%
Contemporary Evolution
20%
Engineering
Load Forecasting
100%
Peak Load
100%
Moving Average
66%
Peak Power
50%
Knowledge Base
16%
Power Supply
16%
Data Preprocessing
16%
Influencing Factor
16%
Electricity Consumption
16%
Comparative Analysis
16%
Additional Reference
16%