TY  - CONF
A1  - Györi, Alexey
A1  - Niederau, Mathis
A1  - Zeller, Violett
A1  - Stich, Volker
A2  - IEEE, 
T1  - Evaluation of Deep Learning-based prediction models in Microgrids
T2  - 2019 IEEE Conference on Energy Conversion (CENCON)
N2  - It is crucial today that economies harness renewable energies and integrate them into the existing grid. Conventionally, energy has been generated based on forecasts of peak and low demands. Renewable energy can neither be produced on demand nor stored efficiently. Thus, the aim of this paper is to evaluate Deep Learning-based forecasts of energy consumption to align energy consumption with renewable energy production. Using a dataset from a use-case related to landfill leachate management, multiple prediction models were used to forecast energy demand.The results were validated based on the same dataset from the recycling industry. Shallow models showed the lowest Mean Absolute Percentage Error (MAPE), significantly outperforming a persistence baseline for both, long-term (30 days), mid-term (7 days) and short-term (1 day) forecasts. A potential decrease of up to 23% in peak energy demand was found that could lead to a reduction of 3,091 kg in CO2-emissions per year. Our approach requires low finanacial investments for energy-management hardware, making it suitable for usage in Small and Medium sized Enterprises (SMEs).
KW  - artificial neural networks
KW  - deep learning
KW  - microgrids
KW  - nonlinear optimization
KW  - peak flattening
KW  - SV7126
Y1  - 2019
UR  - https://epub.fir.de/frontdoor/index/index/docId/16
SN  - 978-1-7281-3433-8
PB  - Curran Associates
CY  - Red Hook (NY)
ER  -