Enhancing accuracy in point-interval load forecasting: A new strategy based on data augmentation, customized deep learning, and weighted linear error correction

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126686 - 126686

Опубликована: Фев. 1, 2025

Язык: Английский

---

A coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110263 - 110263

Опубликована: Март 20, 2025

Язык: Английский

---

XGBoost algorithm assisted multi-component quantitative analysis with Raman spectroscopy

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Год журнала: 2024, Номер 323, С. 124917 - 124917

Опубликована: Июль 31, 2024

Язык: Английский

---

A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125567 - 125567

Опубликована: Окт. 1, 2024

Язык: Английский

---

Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring

Engineering Reports, Год журнала: 2025, Номер 7(2)

Опубликована: Фев. 1, 2025

ABSTRACT Solar energy, a renewable resource, is essential for the efficiency of solar photovoltaic (PV) panels. However, meteorological factors, such as irradiation, weather patterns, precipitation, and overall climate conditions, pose challenges to seamless integration energy production into power grid. Accurate prediction PV system output necessary enhance The study focuses on utilizing machine learning (ML) methodologies accurate forecasting generation, addressing related integrating By analyzing generation data employing advanced ML models, research aims predictability systems. significance this lies in its potential optimize production, improve grid stability, contribute transition towards sustainable sources. This assesses appropriateness approaches accurately projecting half‐hourly cycles next day. consists many analytical phases, including exploratory analysis, inverter which are carried out two separate plants. following step conduct comparative analyses. analyzed using models like gradient boosting classifiers linear regressions. first plant produces best results, with an amazing 0.97% accuracy classifier regression classifier. Contrarily, second achieved 0.61% 0.62% models. study's techniques insights can help operators electricity market stakeholders make informed decisions use generated power, minimize waste, plan preservation, reduce costs, facilitate widespread

Язык: Английский

---

Short-term load forecasting by GRU neural network and DDPG algorithm for adaptive optimization of hyperparameters

Electric Power Systems Research, Год журнала: 2024, Номер 238, С. 111119 - 111119

Опубликована: Сен. 30, 2024

Язык: Английский

---

EDformer family: End-to-end multi-task load forecasting frameworks for day-ahead economic dispatch

Applied Energy, Год журнала: 2025, Номер 383, С. 125319 - 125319

Опубликована: Янв. 15, 2025

Язык: Английский

---

Novel Version of Horse Herd Optimization for Enhancing Electric Load Forecasting Capabilities of Neural Networks

Arabian Journal for Science and Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 8, 2025

Язык: Английский

---

Grid-based market sales forecasting for retail businesses using automated machine learning and geospatial intelligence

Expert Systems with Applications, Год журнала: 2025, Номер 284, С. 127869 - 127869

Опубликована: Апрель 25, 2025

Язык: Английский

---

Amplify seasonality, prioritize meteorological: Strengthening seasonal correlation in photovoltaic forecasting with dual-layer hierarchical attention

Applied Energy, Год журнала: 2025, Номер 394, С. 126104 - 126104

Опубликована: Май 23, 2025

Язык: Английский

---