Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches

• URL: http://arxiv.org/abs/2101.08236v1
• Date: Wed, 20 Jan 2021 18:13:07 GMT
• Title: Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches
• Authors: Vinayak Sharma, Jorge Angel Gonzalez Ordiano, Ralf Mikut, Umit Cali
• Abstract summary: Article presents a comparison between a long short-term memory neural network and other more simple approaches. The paper makes use of an open-source dataset provided during the Global Energy Forecasting Competition of 2014.
• Score: 0.2867517731896504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The high penetration of volatile renewable energy sources such as solar make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open-source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14).

Related papers

• Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning [0.0]
  We develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance and solar power generation. Our results indicate that post-processing substantially improves the solar power generation forecasts, in particular when post-processing is applied to the power predictions.
  arXiv  Detail & Related papers  (2024-06-06T18:08:50Z)
• Denoising diffusion probabilistic models for probabilistic energy forecasting [0.0]
  This paper presents a promising deep learning generative approach called denoising diffusion probabilistic models. It is a class of latent variable models which have recently demonstrated impressive results in the computer vision community. We propose the first implementation of this model for energy forecasting using the open data of the Global Energy Forecasting Competition 2014.
  arXiv  Detail & Related papers  (2022-12-06T13:50:17Z)
• ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference [54.17205151960878]
  We introduce a sampling-free approach that is generic and easy to deploy. We produce reliable uncertainty estimates on par with state-of-the-art methods at a significantly lower computational cost.
  arXiv  Detail & Related papers  (2022-11-21T13:23:09Z)
• Data-Driven Stochastic AC-OPF using Gaussian Processes [54.94701604030199]
  Integrating a significant amount of renewables into a power grid is probably the most a way to reduce carbon emissions from power grids slow down climate change. This paper presents an alternative data-driven approach based on the AC power flow equations that can incorporate uncertainty inputs. The GP approach learns a simple yet non-constrained data-driven approach to close this gap to the AC power flow equations.
  arXiv  Detail & Related papers  (2022-07-21T23:02:35Z)
• An Energy-Based Prior for Generative Saliency [62.79775297611203]
  We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps consistent with human perception.
  arXiv  Detail & Related papers  (2022-04-19T10:51:00Z)
• 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)
• Deep generative modeling for probabilistic forecasting in power systems [34.70329820717658]
  This study uses a recent deep learning technique, the normalizing flows, to produce accurate probabilistic forecasts. We show that our methodology is competitive with other state-of-the-art deep learning generative models.
  arXiv  Detail & Related papers  (2021-06-17T10:41:57Z)
• Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression [51.770998056563094]
  Probabilistic Gradient Boosting Machines (PGBM) is a method to create probabilistic predictions with a single ensemble of decision trees. We empirically demonstrate the advantages of PGBM compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-03T08:32:13Z)
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
  We describe two types of forecasting problems: regular grid-based and graph-based. We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
  arXiv  Detail & Related papers  (2021-05-25T14:35:46Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning [1.52292571922932]
  Short-term solar irradiance forecasting is difficult due to the non-stationary characteristic of solar power. We propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites.
  arXiv  Detail & Related papers  (2019-05-07T14:40:32Z)

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.