Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models

• URL: http://arxiv.org/abs/2211.17017v1
• Date: Wed, 31 Aug 2022 10:40:35 GMT
• Title: Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models
• Authors: Russell Sharp, Hisham Ihshaish, J. Ignacio Deza
• Abstract summary: We discuss limitations of current predictive practices and explore the use of Machine Learning methods to enhance wind ramp event classification and prediction. We additionally outline a design for a novel approach to wind ramp prediction, in which high-resolution wind fields are incorporated to the modelling of wind power.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The forecasting of large ramps in wind power output known as ramp events is crucial for the incorporation of large volumes of wind energy into national electricity grids. Large variations in wind power supply must be compensated by ancillary energy sources which can include the use of fossil fuels. Improved prediction of wind power will help to reduce dependency on supplemental energy sources along with their associated costs and emissions. In this paper, we discuss limitations of current predictive practices and explore the use of Machine Learning methods to enhance wind ramp event classification and prediction. We additionally outline a design for a novel approach to wind ramp prediction, in which high-resolution wind fields are incorporated to the modelling of wind power.

Related papers

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Global Transformer Architecture for Indoor Room Temperature Forecasting [49.32130498861987]
  This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings. It aims at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems. Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings.
  arXiv  Detail & Related papers  (2023-10-31T14:09:32Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer [112.12271800369741]
  Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages. Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system operations. Existing methods are mainly designed for short-term predictions and lack effective spatial-temporal feature augmentation.
  arXiv  Detail & Related papers  (2023-05-30T04:03:15Z)
• Knowledge distillation with error-correcting transfer learning for wind power prediction [6.385624548310884]
  This paper proposes a novel framework with mathematical underpinnings for turbine power prediction. It is developed on favorable knowledge distillation and transfer learning parameters tuning. Results reveal that the proposed framework, developed on favorable knowledge distillation and transfer learning parameters tuning, yields performance boosts from 3.3 % to 23.9 % over its competitors.
  arXiv  Detail & Related papers  (2022-04-01T18:31:47Z)
• Measuring Wind Turbine Health Using Drifting Concepts [55.87342698167776]
  We propose two new approaches for the analysis of wind turbine health. The first method aims at evaluating the decrease or increase in relatively high and low power production. The second method evaluates the overall drift of the extracted concepts.
  arXiv  Detail & Related papers  (2021-12-09T14:04:55Z)
• Uncertainty Set Prediction of Aggregated Wind Power Generation based on Bayesian LSTM and Spatio-Temporal Analysis [42.68418705495523]
  This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms. A Spatio-temporal model is proposed to learn the dynamic features from partial observation in near-surface wind fields of neighboring wind farms. Numerical testing results based on the actual data with 6 wind farms in northwest China show that the uncertainty set of aggregated wind generation is less volatile than that of a single wind farm.
  arXiv  Detail & Related papers  (2021-10-07T11:57:16Z)
• Deep Spatio-Temporal Wind Power Forecasting [4.219722822139438]
  We develop a deep learning approach based on encoder-decoder structure. Our model forecasts wind power generated by a wind turbine using its spatial location relative to other turbines and historical wind speed data.
  arXiv  Detail & Related papers  (2021-09-29T16:26:10Z)
• Wind Power Projection using Weather Forecasts by Novel Deep Neural Networks [0.0]
  Using optimized machine learning algorithms, it is possible to find obscured patterns in the observations and obtain meaningful data. The paper explores the use of both parametric and the non-parametric models for calculating wind power prediction using power curves.
  arXiv  Detail & Related papers  (2021-08-22T17:46:36Z)
• Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential [0.0]
  The wind power estimate presented here represents an important input for planners to support the design of energy systems with increased wind power generation. The methodology is applied to the study of hourly wind power potential on a grid of $250times 250$ m$2$ for turbines of 100 meters hub height in Switzerland.
  arXiv  Detail & Related papers  (2021-07-29T09:52:36Z)
• Machine Intelligent Techniques for Ramp Event Prediction in Offshore and Onshore Wind Farms [0.0]
  Wind resource assessment for onshore and offshore wind farms aids in accurate forecasting and analyzing nature of ramp events. A large ramp event in a short time duration is likely to cause damage to the wind farm connected to the utility grid.
  arXiv  Detail & Related papers  (2020-11-28T21:21:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.