Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather

• URL: http://arxiv.org/abs/2407.19663v2
• Date: Tue, 8 Oct 2024 03:45:17 GMT
• Title: Short-Term Photovoltaic Forecasting Model for Qualifying Uncertainty during Hazy Weather
• Authors: Xuan Yang, Yunxuan Dong, Lina Yang, Thomas Wu,
• Abstract summary: We introduce a modified entropy to qualify uncertainty during hazy weather. clustering and attention mechanisms are employed to reduce computational costs and enhance forecasting accuracy. Experiments on two datasets related to hazy weather demonstrate that our model significantly improves forecasting accuracy.
• Score: 9.367926898177815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Solar energy is one of the most promising renewable energy resources. Forecasting photovoltaic power generation is an important way to increase photovoltaic penetration. However, the difficulty in qualifying the uncertainty of PV power generation, especially during hazy weather, makes forecasting challenging. This paper proposes a novel model to address the issue. We introduce a modified entropy to qualify uncertainty during hazy weather while clustering and attention mechanisms are employed to reduce computational costs and enhance forecasting accuracy, respectively. Hyperparameters were adjusted using an optimization algorithm. Experiments on two datasets related to hazy weather demonstrate that our model significantly improves forecasting accuracy compared to existing models.

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)
• Solarcast-ML: Per Node GraphCast Extension for Solar Energy Production [0.0]
  This project presents an extension to the GraphCast model, a state-of-the-art graph neural network (GNN) for global weather forecasting, by integrating solar energy production forecasting capabilities. The proposed approach leverages the weather forecasts generated by GraphCast and trains a neural network model to predict the ratio of actual solar output to potential solar output based on various weather conditions. The results demonstrate the model's effectiveness in accurately predicting solar radiation, with its convergence behavior, decreasing training loss, and accurate prediction of solar radiation patterns.
  arXiv  Detail & Related papers  (2024-06-19T13:47:05Z)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• 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)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• Diffusion Models for High-Resolution Solar Forecasts [0.0]
  Score-based diffusion models offer a new approach to modeling probability distributions over many dependent variables. We apply the technique to day-ahead solar irradiance forecasts by generating many samples from a diffusion model trained to super-resolve numerical weather predictions.
  arXiv  Detail & Related papers  (2023-02-01T01:32:25Z)
• A Hybrid Model for Forecasting Short-Term Electricity Demand [59.372588316558826]
  Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
  arXiv  Detail & Related papers  (2022-05-20T22:13:25Z)
• Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models [14.579720180539136]
  We train and evaluate the models using public data from seven stations in the SURFRAD network. We show that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model.
  arXiv  Detail & Related papers  (2020-10-09T17:57:59Z)
• Short-term prediction of photovoltaic power generation using Gaussian process regression [3.8386504037654534]
  This paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom. The model is evaluated for short-term forecasts of 48 hours against three main factors. We also compare very short-term forecasts in terms of cloud coverage within the prediction period and only initial cloud coverage as a predictor.
  arXiv  Detail & Related papers  (2020-10-05T18:35:25Z)

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.