BWO–ICEEMDAN–iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization DOI Creative Commons

Danqi Zheng,

Jiyun Qin,

Zhen Liu

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(5), P. 243 - 243

Published: April 24, 2025

Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A response program can adjust load from side to promote balance of demand. Load forecasting facilitate implementation this program. However, as consumption patterns become more diverse, resulting data grows increasingly irregular, making precise difficult. Therefore, paper developed specialized scheme. First, parameters improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, nonlinear decomposed into multiple subsequences ICEEMDAN. Finally, each subsequence was independently predicted iTransformer model, overall forecast derived by integrating these individual predictions. Data Singapore selected for validation. The results showed that BWO–ICEEMDAN–iTransformer model outperformed other comparison models, an R2 0.9873, RMSE 48.0014, MAE 66.2221.

Language: Английский

Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties DOI Creative Commons
Xinyu Wang, Jiandong Wang,

Mengyao Wei

et al.

Entropy, Journal Year: 2025, Volume and Issue: 27(1), P. 52 - 52

Published: Jan. 9, 2025

In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for system in process, aiming to achieve optimal allocation of gas, steam, and electricity ensure executability under modeling uncertainties. First, mechanistic models are developed major chemical equipments, including the desulfurization, steam boilers, air separation, syngas compressors. Structural errors these varying operating conditions result noticeable model Second, Bayesian estimation theory Markov Chain Monte Carlo approach employed analyze differences between historical data predictions conditions, thereby quantifying Finally, subject constraints uncertainties, equipment capacities, balance, multi-objective formulated minimize gas loss, costs. The entropy weight then applied filter Pareto front solution set, selecting final with minimal subjectivity preferences. Case studies using Aspen Hysys-based simulations show that solutions considering uncertainties outperform counterparts from standard deterministic terms executability.

Language: Английский

Citations

0
Retinal Fundus Imaging-Based Diabetic Retinopathy Classification using Transfer Learning and Fennec Fox Optimization DOI Creative Commons
Indresh Kumar Gupta, Shruti Patil, Supriya Mahadevkar

et al.

MethodsX, Journal Year: 2025, Volume and Issue: 14, P. 103232 - 103232

Published: Feb. 17, 2025

Language: Английский

Citations

0
Optimal Scheduling of Energy Systems for Gas-to-Methanol Processes Using Operating Zone Models and Entropy Weights DOI Creative Commons
Xinyu Wang,

Mengyao Wei,

Jiandong Wang

et al.

Entropy, Journal Year: 2025, Volume and Issue: 27(3), P. 324 - 324

Published: March 20, 2025

In coal chemical industries, the optimal allocation of gas and steam is crucial for enhancing production efficiency maximizing economic returns. This paper proposes an scheduling method using operating zone models entropy weights energy system in a gas-to-methanol process. The first step to develop mechanistic main facilities methanol production, namely desulfurization, air separation, syngas compressors, boilers. A genetic algorithm employed estimate unknown parameters models. These are grounded physical mechanisms such as conservation, mass thermodynamic laws. multi-objective optimization problem formulated, with objectives minimizing loss, costs. required constraints include equipment capacities, balance, coupling relationships. then convert this into single-objective problem. second solve based on model, which describes high-dimensional geometric space consisting all steady-state data points that satisfy operation constraints. By projecting model decision variable plane, solution obtained visual manner contour lines auxiliary lines. Case studies Aspen Hysys used support validate effectiveness proposed method.

Language: Английский

Citations

0
Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images DOI Creative Commons

Khaled Tarmissi,

Jamal Alsamri, Mashael Maashi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 24, 2025

Language: Английский

Citations

0
BWO–ICEEMDAN–iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization DOI Creative Commons

Danqi Zheng,

Jiyun Qin,

Zhen Liu

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(5), P. 243 - 243

Published: April 24, 2025

Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A response program can adjust load from side to promote balance of demand. Load forecasting facilitate implementation this program. However, as consumption patterns become more diverse, resulting data grows increasingly irregular, making precise difficult. Therefore, paper developed specialized scheme. First, parameters improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, nonlinear decomposed into multiple subsequences ICEEMDAN. Finally, each subsequence was independently predicted iTransformer model, overall forecast derived by integrating these individual predictions. Data Singapore selected for validation. The results showed that BWO–ICEEMDAN–iTransformer model outperformed other comparison models, an R2 0.9873, RMSE 48.0014, MAE 66.2221.

Language: Английский

Citations

0