A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms

• URL: http://arxiv.org/abs/2302.01222v5
• Date: Tue, 5 Sep 2023 15:02:04 GMT
• Title: A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms
• Authors: Meiyu Jiang, Jun Shen, Xuetao Jiang, Lihui Luo, Rui Zhou, Qingguo Zhou
• Abstract summary: Wind power generation is characterized by volatility, intermittence, and randomness. Traditional wind power forecasting systems primarily focus on ultra-short-term or short-term forecasts. We propose an automatic framework capable of forecasting wind power across multi-time scale.
• Score: 6.120692237856329
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wind energy is a widely distributed, renewable, and environmentally friendly energy source that plays a crucial role in mitigating global warming and addressing energy shortages. Nevertheless, wind power generation is characterized by volatility, intermittence, and randomness, which hinder its ability to serve as a reliable power source for the grid. Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources. However, traditional wind power forecasting systems primarily focus on ultra-short-term or short-term forecasts, limiting their ability to address the diverse adjustment requirements of the power system simultaneously. To overcome these challenges, We propose an automatic framework capable of forecasting wind power across multi-time scale. The framework based on the tree-structured Parzen estimator (TPE) and temporal fusion transformer (TFT) that can provide ultra-short-term, short-term and medium-term wind power forecasting power.Our approach employs the TFT for wind power forecasting and categorizes features based on their properties. Additionally, we introduce a generic algorithm to simultaneously fine-tune the hyperparameters of the decomposition method and model. We evaluate the performance of our framework by conducting ablation experiments using three commonly used decomposition algorithms and six state-of-the-art models for forecasting multi-time scale. The experimental results demonstrate that our proposed method considerably improves prediction accuracy on the public dataset Engie https://opendata-renewables.engie.com. Compared to the second-best state-of-the-art model, our approach exhibits a reduction of 31.75% and 28.74% in normalized mean absolute error (nMAE) for 24-hour forecasting, and 20.79% and 16.93% in nMAE for 48-hour forecasting, respectively.

Related papers

• Hiformer: Hybrid Frequency Feature Enhancement Inverted Transformer for Long-Term Wind Power Prediction [6.936415534357298]
  We propose a novel approach called Hybrid Frequency Feature Enhancement Inverted Transformer (Hiformer) Hiformer integrates signal decomposition technology with weather feature extraction technique to enhance the modeling of correlations between meteorological conditions and wind power generation. Compared to the state-of-the-art methods, Hiformer: (i) can improve the prediction accuracy by up to 52.5%; and (ii) can reduce computational time by up to 68.5%.
  arXiv  Detail & Related papers  (2024-10-17T08:00:36Z)
• Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data [4.048814984274799]
  This paper proposes a novel methodology for deterministic wind and solar energy generation forecasting for multiple generation sites. The method employs a U-shaped Temporal Convolutional Auto-Encoder architecture for temporal processing of weather-related and energy-related time-series. The conducted experimental evaluation on a day-ahead solar and wind energy forecasting scenario on five datasets demonstrated that the proposed method achieves top results.
  arXiv  Detail & Related papers  (2024-04-26T09:30:55Z)
• Bias correction of wind power forecasts with SCADA data and continuous learning [0.0]
  We present, evaluate, and compare four machine learning-based wind power forecasting models. The models are evaluated on datasets from a wind park comprising 65 wind turbines.
  arXiv  Detail & Related papers  (2024-02-21T16:31:45Z)
• 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)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case [3.3788638227700734]
  This work presents concepts and approaches concerning probabilistic forecasts with deep learning. Deep learning models are used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway.
  arXiv  Detail & Related papers  (2022-03-10T15:52:11Z)
• Calibration of wind speed ensemble forecasts for power generation [0.0]
  In the last decades wind power became the second largest energy source in the EU covering 16% of its electricity demand. Due to its volatility, accurate short range wind power predictions are required for successful integration of wind energy into the electrical grid. We show that compared with the raw ensemble, post-processing always improves the calibration of probabilistic and accuracy of point forecasts.
  arXiv  Detail & Related papers  (2021-04-30T11:18:03Z)
• Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids [67.72249211312723]
  Real-time state estimation and forecasting is critical for efficient operation of power grids. PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
  arXiv  Detail & Related papers  (2020-10-09T14:18:31Z)

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.