Energy Forecasting: A Comprehensive Review of Techniques and Technologies
Distribution System Operators (DSOs) and Aggregators benefit from novel energy
forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with …
forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with …
Energy load forecasting: One-step ahead hybrid model utilizing ensembling
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 …
(ML) techniques can provide accurate forecasts, which enable efficient scheduling and …
Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models
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 …
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 …
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 …
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 …
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 …
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
Background/Objectives: This study addresses the complex challenge of Nurse Rostering
(NR) in oncology departments, a critical component of healthcare management affecting …
(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 …
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 …
regression models, including Linear Regression (LR), Random Forest (RF), Multi-Layer …