AI-coherent data-driven forecasting model for a combined cycle power plant
This study investigates the transformation of energy models to align with machine learning requirements as a promising tool for optimizing the operation of combined cycle power plants (CCPPs). By modeling energy production as a function of environmental and control variables, this methodology offers an innovative way to achieve energy-efficient power generation in the context of the data-driven application. This study focuses on developing a thorough AI-coherent modeling approach for CCPP optimization, preferring an interdisciplinary perspective and coming up with a comprehensive, insightful analysis. The proposed numerical model using Broyden Fletcher Goldfarb Shanno (BFGS) algorithm enhances efficiency by simulating various operating scenarios and adjusting optimal parameters, leading to a high yield power generation of 2.23% increase from 452 MW to 462.1 MW by optimizing the environmental factors. This study deals with data-driven modeling based on historical data to make predictions without prior knowledge of the system's parameter, demonstrating several merits in identifying patterns that can be difficult for human analysts to detect, high accuracy when trained on large datasets, and the potential to improve over time with new data. The proposed modeling approach and methodology can be expanded as a valuable tool for forecasting and decision-making in complex energy systems.
Code (1)
Tasks
Decision MakingModel OptimizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Improving Motion Forecasting for Autonomous Driving with the Cycle Consistency Loss
Robust motion forecasting of the dynamic scene is a critical component of an autonomous vehicle. It is a challenging problem due to the heterogeneity in the scene and the inherent uncertainties in the problem. To improve…
Autonomous DrivingMotion ForecastingGeometric Semantic Genetic Programming Algorithm and Slump Prediction
Research on the performance of recycled concrete as building material in the current world is an important subject. Given the complex composition of recycled concrete, conventional methods for forecasting slump scarcely …
PredictionProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
Designing proteins that satisfy natural language functional requirements is a central goal in protein engineering. A straightforward baseline is to fine-tune generic instruction-tuned LLMs as direct text-to-sequence gene…
Reinforcement LearningProtein DesignUnified Zero-Shot Time Series Forecasting: A Darts Foundation
Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis. A series of foundation models have recently claimed accuracy improvements in zero-shot forecasting, p…
Time Series ForecastingTime Series AnalysisAn Axiomatic Characterization of Split Cycle
A number of rules for resolving majority cycles in elections have been proposed in the literature. Recently, Holliday and Pacuit (Journal of Theoretical Politics 33 (2021) 475-524) axiomatically characterized the class o…