Energy Forecasting: A Comprehensive Review of Techniques and Technologies

A Mystakidis, P Koukaras, N Tsalikidis, D Ioannidis… - Energies, 2024 - mdpi.com
Distribution System Operators (DSOs) and Aggregators benefit from novel energy
forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with …

Energy load forecasting: One-step ahead hybrid model utilizing ensembling

N Tsalikidis, A Mystakidis, C Tjortjis, P Koukaras… - Computing, 2024 - Springer
In the light of the adverse effects of climate change, data analysis and Machine Learning
(ML) techniques can provide accurate forecasts, which enable efficient scheduling and …

Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

P Koukaras, A Mustapha, A Mystakidis, C Tjortjis - Energies, 2024 - mdpi.com
The building sector, known for its high energy consumption, needs to reduce its energy use
due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy …

Power Load Forecasting: A Time-Series Multi-Step Ahead and Multi-Model Analysis

A Mystakidis, N Tsalikidis, P Koukaras… - 2023 58th …, 2023 - ieeexplore.ieee.org
Distribution System Operators and Aggregators can derive benefits from innovative
approaches in Power or Energy Load Forecasting (PLF-ELF). Enhanced accuracy in PLF …

Traffic congestion prediction and missing data: a classification approach using weather information

A Mystakidis, C Tjortjis - International Journal of Data Science and …, 2024 - Springer
Traffic congestion in major cities is becoming increasingly severe. Numerous academic and
commercial initiatives were conducted over the past decades to address this challenge …

Data Mining for Smart Cities: Traffic Congestion Prediction

A Mystakidis, O Geromichalou… - 2023 14th International …, 2023 - ieeexplore.ieee.org
In this work, we utilized univariable and multivari-able regression models, including Linear
Regression (LR), Ran-dom Forest (RF), Multi-Layer Perceptron (MLP), and Gradient …

Hybrid CNN-LSTM Forecasting Model for Electric Vehicle Charging Demand in Smart Buildings

N Tsalikidis, P Koukaras, D Ioannidis… - 2024 6th Global …, 2024 - ieeexplore.ieee.org
The accelerated shift towards renewable energy sources has signalled the widespread
adoption of Electric Vehicles (EVs) as the primary mode of transportation. Concurrently …

Optimizing Nurse Rostering: A Case Study Using Integer Programming to Enhance Operational Efficiency and Care Quality

A Mystakidis, C Koukaras, P Koukaras, K Kaparis… - Healthcare, 2024 - mdpi.com
Background/Objectives: This study addresses the complex challenge of Nurse Rostering
(NR) in oncology departments, a critical component of healthcare management affecting …

Graph Databases in Smart City Applications–Using Neo4j and Machine Learning for Energy Load Forecasting 7

A Mystakidis - Graph Databases, 2023 - taylorfrancis.com
The smart city (SC) approach aims to enhance populations' lives through developments in
knowledge and connectivity systems such as traffic congestion management, Energy …

Traffic Congestion Prediction: A Machine Learning Approach

O Geromichalou, A Mystakidis, C Tjortjis - International Conference on …, 2023 - Springer
In this study, in order to forecast traffic flow, we employed univariable and multivariable
regression models, including Linear Regression (LR), Random Forest (RF), Multi-Layer …