[HTML][HTML] Optimized hybrid ensemble learning approaches applied to very short-term load forecasting
The significance of accurate short-term load forecasting (STLF) for modern power systems'
efficient and secure operation is paramount. This task is intricate due to cyclicity, non …
efficient and secure operation is paramount. This task is intricate due to cyclicity, non …
Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks
Modern power grids are faced with a series of challenges, such as the ever-increasing
demand for renewable energy sources, extensive urbanization, climate and energy crisis …
demand for renewable energy sources, extensive urbanization, climate and energy crisis …
Analysis for non-residential short-term load forecasting using machine learning and statistical methods with financial impact on the power market
Short-term load forecasting predetermines how power systems operate because electricity
production needs to sustain demand at all times and costs. Most load forecasts for the non …
production needs to sustain demand at all times and costs. Most load forecasts for the non …
Forecasting sustainable development goals scores by 2030 using machine learning models
Abstract The Sustainable Development Goals (SDGs) set by the United Nations are a
worldwide appeal to eliminate poverty, preserve the environment, address climate change …
worldwide appeal to eliminate poverty, preserve the environment, address climate change …
Identification of nontechnical losses in distribution systems adding exogenous data and artificial intelligence
MB Capeletti, BK Hammerschmitt, RG Negri… - Energies, 2022 - mdpi.com
Nontechnical losses (NTL) are irregularities in the consumption of electricity and mainly
caused by theft and fraud. NTLs can be characterized as outliers in historical data series …
caused by theft and fraud. NTLs can be characterized as outliers in historical data series …
Improving accuracy of machine learning based short-term load forecasting models with correlation analysis and feature engineering
N Sergeev, P Matrenin - 2023 IEEE 24th International …, 2023 - ieeexplore.ieee.org
Short-term load forecasting is an integral part of the electric power system management and
is necessary to ensure the electricity market operation. Load forecasting of individual market …
is necessary to ensure the electricity market operation. Load forecasting of individual market …
Distance Metrics for Evaluating the Use of Exogenous Data in Load Forecasting
Similarity metrics measure distance to a compared time series. It allows for a classification
and dependency search. These metrics are used for the selection of additional time series in …
and dependency search. These metrics are used for the selection of additional time series in …
CNN-N-BEATS: Novel Hybrid Model for Time-Series Forecasting
K Aiwansedo, J Bosche, W Badreddine… - … Conference on Deep …, 2024 - Springer
Time-series forecasting (TS) is a vital tool for scientific study and has many applications in a
wide range of disciplines, including engineering, economics, finance, environmental …
wide range of disciplines, including engineering, economics, finance, environmental …
Information Processing and Management of Uncertainty in Knowledge-Based Systems
We are very pleased to present you with the proceedings of the 19th International
Conference on Information Processing and Management of Uncertainty in Knowledge …
Conference on Information Processing and Management of Uncertainty in Knowledge …
[CITAS][C] Local Communities and Electricity Markets: Forecast of net consumption
CFS Marcelino - 2024