Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market

Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market

This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an alwa...

Full description

Saved in:

Bibliographic Details
Main Authors:	Nikolaos Koutantos, Maria Fotopoulou, Dimitrios Rakopoulos
Format:	Article
Language:	English
Published:	MDPI AG 2024-10-01
Series:	Applied Sciences
Subjects:	ensemble-based model automated machine learning hyperparameter optimization load forecasting
Online Access:	https://www.mdpi.com/2076-3417/14/21/9766
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market
by: Vasileios Laitsos, et al.
Published: (2024-11-01)

Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model
by: Shuang Zeng, et al.
Published: (2025-01-01)

Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review
by: Umme Mumtahina, et al.
Published: (2024-10-01)

Using crafted features and polar bear optimization algorithm for short-term electric load forecast system
by: Mansi Bhatnagar, et al.
Published: (2025-01-01)

The Use of Ensembles in Space Weather Forecasting
by: J. A. Guerra, et al.
Published: (2020-02-01)

Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
by: Hyasseliny A. Hurtado-Mora, et al.
Published: (2024-10-01)

Optimizing deep neural network architectures for renewable energy forecasting
by: Sunawar khan, et al.
Published: (2024-11-01)

Electrical Load Forecasting Models for Different Generation Modalities: A Review
by: Abdul Azeem, et al.
Published: (2021-01-01)

Photovoltaic penetration potential in the Greek island of Ikaria
by: Maria Fotopoulou, et al.
Published: (2024-12-01)

HP-SEFB: hyper parameterized stacking ensemble with fuzzy-Boruta for rheumatoid arthritis classification
by: D. Jeyamani, et al.
Published: (2024-12-01)

Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting
by: Winita Sulandari, et al.
Published: (2025-01-01)

Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features
by: Sunawar Khan, et al.
Published: (2024-12-01)

Adapting Ensemble‐Calibration Techniques to Probabilistic Solar‐Wind Forecasting
by: N. O. Edward‐Inatimi, et al.
Published: (2024-12-01)

Ensemble machine learning model for forecasting wind farm generation
by: A. G. Rusina, et al.
Published: (2024-04-01)

System of automated monitoring and forecasting of the remaining resource of pipeline transport
by: S. O. Gaponenko, et al.
Published: (2024-04-01)

Fusion ConvLSTM-Net: Using Spatiotemporal Features to Increase Residential Load Forecast Horizon
by: Abhishu Oza, et al.
Published: (2025-01-01)

Improving long short-term memory (LSTM) networks for arbitrage spread forecasting: integrating cuckoo and zebra algorithms in chaotic mapping space for enhanced accuracy
by: Mingfu Zhu, et al.
Published: (2024-12-01)

Application and Verification of Convective Scale Ensemble Forecast for a Heavy Precipitation Event That Occurred in Eastern Southwest China
by: Lianglyu Chen
Published: (2025-07-01)

Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
by: Youngjin Han, et al.
Published: (2024-10-01)

An efficient load forecasting technique by using Holt‐Winters and Prophet algorithms to mitigate the impact on power consumption in COVID‐19
by: Waqar Waheed, et al.
Published: (2024-12-01)

Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
by: Muhammed Sütçü, et al.
Published: (2022-02-01)

An EV load forecasting method for using GCN-LSTM
by: HUANG Jian, et al.
Published: (2024-12-01)

Personalized Federated Learning for Heterogeneous Residential Load Forecasting
by: Xiaodong Qu, et al.
Published: (2023-12-01)

An Intelligent System for Load Forecasting on a Distribution Network
by: Saheed Gbadamosi, et al.
Published: (2021-06-01)

Enhancing Load Prediction Accuracy using Optimized Support Vector Regression Models
by: Abdulsemiu Olawuyi, et al.
Published: (2023-12-01)

Optimizing a Machine Learning Algorithm by a Novel Metaheuristic Approach: A Case Study in Forecasting
by: Bahadır Gülsün, et al.
Published: (2024-12-01)

Ensemble-based model to investigate factors influencing road crash fatality for imbalanced data
by: Nazmus Sakib, et al.
Published: (2024-12-01)

Predicting wind power using LSTM, Transformer, and other techniques
by: Arun Kumar M, et al.
Published: (2024-12-01)

Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL
by: WANG Yongli, LIU Zeqiang, DONG Huanran, LI Dexin, CHEN Xin, GUO Lu, WANG Jiarui
Published: (2025-01-01)

Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air
by: Jiayu Yang, et al.
Published: (2025-01-01)

A Cross-Regional Load Forecasting Method Based on a Pseudo-Distributed Federated Learning Strategy
by: Jinsong Deng, et al.
Published: (2025-01-01)

Effect of the Ensemble System Size on the Precipitation Forecast Accuracy
by: Atefeh Mohamadi, et al.
Published: (2022-09-01)

Optimizing Models and Data Denoising Algorithms for Power Load Forecasting
by: Yanxia Li, et al.
Published: (2024-11-01)

Advanced Machine Learning Ensembles for Improved Precipitation Forecasting: The Modified Stacking Ensemble Strategy in China
by: Tiantian Tang, et al.
Published: (2025-01-01)

Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting
by: S. V. Sholtanyuk
Published: (2019-08-01)

Forecasting the daily energy load schedule of working days using meteofactors for the central power system of Mongolia
by: A. G. Rusina, et al.
Published: (2022-06-01)

Forecasting the Sugarcane Yields Based on Meteorological Data Through Ensemble Learning
by: Sumit Kumar, et al.
Published: (2024-01-01)

A Short-Term Power Load Forecasting Method Using CNN-GRU with an Attention Mechanism
by: Qingbo Hua, et al.
Published: (2024-12-01)

Assessing global ensemble systems’ forecasts of tropical cyclone genesis in differing environmental flow regimes in the western North Pacific
by: Yasuhiro Kawabata, et al.
Published: (2024-12-01)

A Regression-Based Method for Monthly Electric Load Forecasting in South Korea
by: Geun-Cheol Lee
Published: (2024-11-01)