A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality
Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and smart energy pricing. The data collection process for client consumption mode...
Main Authors: | Dimitrios Kontogiannis, Dimitrios Bargiotas, Aspassia Daskalopulu, Lefteri H. Tsoukalas |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-09-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/14/19/6088 |
Similar Items
-
Structural Ensemble Regression for Cluster-Based Aggregate Electricity Demand Forecasting
by: Dimitrios Kontogiannis, et al.
Published: (2022-10-01) -
Error Compensation Enhanced Day-Ahead Electricity Price Forecasting
by: Dimitrios Kontogiannis, et al.
Published: (2022-02-01) -
Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
by: Athanasios Ioannis Arvanitidis, et al.
Published: (2021-11-01) -
Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting
by: Athanasios Ioannis Arvanitidis, et al.
Published: (2022-02-01) -
Enhanced Automated Deep Learning Application for Short-Term Load Forecasting
by: Vasileios Laitsos, et al.
Published: (2023-06-01)