Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization

Expert Systems with Applications, Год журнала: 2024, Номер 245, С. 123088 - 123088

Опубликована: Янв. 2, 2024

Язык: Английский

---

Accelerating renewables: Unveiling the role of green energy markets

Applied Energy, Год журнала: 2024, Номер 366, С. 123286 - 123286

Опубликована: Апрель 29, 2024

Язык: Английский

---

Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction

Energy, Год журнала: 2024, Номер 293, С. 130538 - 130538

Опубликована: Фев. 3, 2024

Язык: Английский

---

A hybrid wind speed forecasting model with rolling mapping decomposition and temporal convolutional networks

Energy, Год журнала: 2025, Номер 324, С. 135673 - 135673

Опубликована: Апрель 23, 2025

Язык: Английский

---

A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Май 27, 2024

Язык: Английский

---

Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula

Applied Soft Computing, Год журнала: 2024, Номер 164, С. 112007 - 112007

Опубликована: Июль 17, 2024

Язык: Английский

---

An Adaptive Strategy for Wind Speed Forecasting Under Functional Data Horizon: A Way Toward Enhancing Clean Energy

IEEE Access, Год журнала: 2024, Номер 12, С. 68730 - 68746

Опубликована: Янв. 1, 2024

An important issue in competitive energy markets is the accurate and efficient wind speed forecasting for power production. However, models developed one location usually do not match other site various reasons like changes terrain, different patterns, atmospheric factors such as temperature, pressure, humidity, etc. Thus, introducing a flexible model that captures all features challenging task. This paper proposes functional data analysis (FDA) approach to forecast variant daily profiles with higher accuracy. Unlike traditional methods, FDA more attractive it forecasts complete profile, thus, can be obtained ultra-short period. To this end, first filtered extreme values. The series then divided into deterministic (Component-I) stochastic (Component-II) components. Component-I modeled forecasted based on generalized additive modeling technique. On hand, Component-II using autoregressive (FAR) FAR explanatory variables (FARX). For comparison purposes, from univariate integrated moving average (ARIMA), seasonal ARIMA (SARIMA), SARIMA exogenous information (SARIMAX), neural network (NNAR) are also obtained. empirical analysis, NASA project Canada located Durham, England, one-day-ahead out-of-sample year. performance of assessed through accuracy measures, namely mean error, root squared absolute standard error. results indicate outperform classical ARIMA, SARIMA, SARIMAX, deep learning model, NNAR. Within models, ability FARX superior FAR.

Язык: Английский

---

Complexity-aided time series modeling and forecasting under a decomposition-aggregation framework

Information Sciences, Год журнала: 2024, Номер 664, С. 120352 - 120352

Опубликована: Фев. 23, 2024

Язык: Английский

---

A Survey of Artificial Intelligence Applications in Wind Energy Forecasting

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Сен. 13, 2024

Язык: Английский

---

Environmental policy-driven electricity consumption prediction: A novel buffer-corrected Hausdorff fractional grey model informed by two-stage enhanced multi-objective optimization

Journal of Environmental Management, Год журнала: 2025, Номер 377, С. 124540 - 124540

Опубликована: Фев. 24, 2025

Язык: Английский

---