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Data needs for load forecasting at different aggregation le..:

, In: 13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2022),

Qi, S. ; Ponoćko, J. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.3300

RT 
T1 <cTitle>13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2022)</cTitle>: T1 <Title>Data needs for load forecasting at different aggregation levels using LSTM networks</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10137609&Exemplar=1&LAN=DE
				
A1 Qi, S.
A1 Ponoćko, J.
YR 2022
K1 Gaussian processes
K1 load forecasting
K1 power consumption
K1 power engineering computing
K1 power grids
K1 recurrent neural nets
K1 aggregation level
K1 different aggregation levels
K1 distributed energy resources
K1 economic benefits
K1 existing energy system
K1 forecasting performance
K1 input data combination
K1 input data combinations
K1 load forecasting approaches
K1 LSTM networks
K1 mass introduction
K1 network planning
K1 optimal forecasting model
K1 power consumption data
K1 power grid
K1 renewable energies
K1 short-term load forecasting method
K1 supply
K1 training data types
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.3300
DO https://doi.org/10.1049/icp.2022.3300
SF ELIB - SuUB Bremen

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