Advanced Methods of Power Load Forecasting
Object category:
Elektronische Ressource
Person/Institution:
Publisher:
MDPI - Multidisciplinary Digital Publishing Institute
Place of publication:
Basel
Date:
2022
Extent, illustration, format:
1 Online-Ressource (128 p.)
Language:
Nicht zu entscheiden
Providing institution:
Additional information
Abstract:
This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load
Object text:
English
Access and usage options
Contact
Universität Erfurt
Forschungsbibliothek Gotha
Schloss Friedenstein
Schlossplatz 1
99867 Gotha
+49 361 737-5540
bibliothek.gotha(at)uni-erfurt.de
Forschungsbibliothek Gotha
Schloss Friedenstein
Schlossplatz 1
99867 Gotha
+49 361 737-5540
bibliothek.gotha(at)uni-erfurt.de
Administrative details
Created:
2023-04-12
Last changed:
2023-01-29
Added to portal:
2023-04-12
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