Short-term Interval Prediction Strategy of Photovoltaic Power Based on Meteorological Reconstruction with Spatiotemporal Correlation and Multi-factor Interval Constraints

Renewable Energy, Journal Year: 2024, Volume and Issue: 237, P. 121834 - 121834

Published: Nov. 6, 2024

Language: Английский

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Development of data-driven estimation models of village carbon emissions by built form factors: The study in Huaihe River Basin, China

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112846 - 112846

Published: March 1, 2025

Language: Английский

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Research on Photovoltaic Power Prediction Method Based on Dynamic Similar Selection and Bidirectional Gated Recurrent Unit

Advanced Theory and Simulations, Journal Year: 2025, Volume and Issue: unknown

Published: March 8, 2025

Abstract Photovoltaic (PV) power generation is vital for sustainable energy development, yet its inherent randomness and volatility challenge grid stability. Accurate short‐term PV prediction essential reliable operation. This paper proposes an integrated method combining dynamic similar selection (DSS), variational mode decomposition (VMD), bidirectional gated recurrent unit (BiGRU), improved sparrow search algorithm (ISSA). First, DSS selects training data based on local meteorological similarity, reducing interference. VMD then decomposes into smooth components, mitigating volatility. The Pearson correlation coefficient used to filter highly relevant variables, enhancing input quality. BiGRU captures temporal evolution patterns, with ISSA optimizing key parameters robust forecasting. Validated historical Australian under diverse weather conditions, the proposed effectively reduces volatility, significantly improving accuracy reliability. These advancements support stable supply efficient

Language: Английский

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A Two-Layer Cooperative Optimization Approach for Coordinated Photovoltaic-Energy Storage System Sizing and Factory Energy Dispatch Under Industrial Load Profiles

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2713 - 2713

Published: March 19, 2025

Driven by policy incentives and economic pressures, energy-intensive industries are increasingly focusing on energy cost reductions amid the rapid adoption of renewable energy. However, existing studies often isolate photovoltaic-energy storage system (PV-ESS) configurations from detailed load scheduling, limiting industrial park management. To address this, we propose a two-layer cooperative optimization approach (TLCOA). The upper layer employs genetic algorithm (GA) to optimize PV capacity sizing through natural selection crossover operations, while lower utilizes mixed integer linear programming (MILP) derive cost-minimized scheduling strategies under time-of-use tariffs. Multi-process parallel computing accelerates fitness evaluations, resolving high-dimensional data challenges. is introduced accelerate effectively addressing challenges posed data. Validated with real power market data, TLCOA demonstrated adaptation fluctuations achieving 23.68% improvement in computational efficiency, 1.73% reduction investment costs, 7.55% decrease purchase 8.79% enhancement utilization compared traditional methods. This integrated framework enables cost-effective PV-ESS deployment adaptive management facilities, offering actionable insights for integration scalable optimization.

Language: Английский

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Mechanical Characteristic Fault Diagnosis of High Voltage Circuit Breaker Based on Multi-Signal Fusion and CatBoost

IETE Journal of Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: April 27, 2025

Language: Английский

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