Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes

• URL: http://arxiv.org/abs/2302.00388v2
• Date: Thu, 30 Mar 2023 15:12:17 GMT
• Title: Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
• Abstract summary: We consider Gaussian processes (GPs) for modelling and predicting solar photovoltaic energy production in the UK. A standard application of GP regression on the PV timeseries data is infeasible due to the large data size and non-Gaussianity of PV readings. This is made possible by leveraging recent advances in scalable GP inference, in particular, by using the state-space form of GPs, combined with modern variational inference techniques.
• Score: 11.16908638844702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management. Ideally these forecasts are equipped with error bars, so that downstream decisions can account for uncertainty. To produce predictions with error bars in this setting, we consider Gaussian processes (GPs) for modelling and predicting solar photovoltaic energy production in the UK. A standard application of GP regression on the PV timeseries data is infeasible due to the large data size and non-Gaussianity of PV readings. However, this is made possible by leveraging recent advances in scalable GP inference, in particular, by using the state-space form of GPs, combined with modern variational inference techniques. The resulting model is not only scalable to large datasets but can also handle continuous data streams via Kalman filtering.

Related papers

• An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting [3.5367752722811794]
  We develop a deployment-oriented environmental-AI pipeline for day-ahead hourly PV forecasting.<n>The pipeline corrects timestamps conventions, constructs leakage-safe solar-geometry and clearness-index features, and adds short-term atmospheric context.<n>It reduces daylight normalised RMSE by about 32% under random day-blocked evaluation and 9% under the stricter rolling-origin protocol.
  arXiv  Detail & Related papers  (2026-08-03T11:48:32Z)
• AI and Open-data Driven Scalable Solar Power Profiling [48.72868465736126]
  This paper presents an open, scalable framework for detecting solar panels from open data and generating city-level solar power profiles.<n>We leverage foundation vision AI models to detect solar panel from open-source satellite imagery.<n>The framework reduces dependency on proprietary imagery, manual labeling, and closed-source models, and offers a transparent and scalable approach for solar planning and analysis.
  arXiv  Detail & Related papers  (2026-05-04T15:37:52Z)
• Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables [59.37476887007663]
  We develop ApolloPFN, a prior-data fitted network (PFN) that is time-aware (unlike prior PFNs)<n>Our design introduces two major advances: (i) a synthetic data generation procedure tailored to resolve the failure modes that arise when PFNs are applied to time series; and (ii) time-aware architectural modifications that embed inductive biases needed to exploit the time series context.
  arXiv  Detail & Related papers  (2026-03-16T18:33:23Z)
• VFMF: World Modeling by Forecasting Vision Foundation Model Features [67.09340259579761]
  We introduce a generative forecaster that performs autoregressive flow matching in vision foundation models feature space.<n>We show that this latent information more effectively than previously used PCA-based alternatives, both for forecasting and other applications.<n>With matched architecture and compute, our method produces sharper and more accurate predictions than regression across all modalities.
  arXiv  Detail & Related papers  (2025-12-12T02:10:05Z)
• Ultra-short-term solar power forecasting by deep learning and data reconstruction [60.200987006598524]
  We propose a deep-learning based ultra-short-term solar power prediction with data reconstruction.<n>We employ deep-learning models to capture long- and short-term dependencies towards the target prediction period.
  arXiv  Detail & Related papers  (2025-09-21T14:22:35Z)
• SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe [0.0]
  SolNet is a novel, general-purpose, multivariate solar power forecaster. We show that SolNet improves forecasting performance over data-scarce settings. We provide guidelines and considerations for transfer learning practitioners.
  arXiv  Detail & Related papers  (2024-05-23T12:00:35Z)
• 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)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• 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)
• Feature Construction and Selection for PV Solar Power Modeling [1.8960797847221296]
  Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages. The solar power output is time-series data dependent on many factors, such as irradiance and weather. A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data.
  arXiv  Detail & Related papers  (2022-02-13T06:49:28Z)
• A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning [4.844946519309793]
  We develop a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Our model estimates a location's future solar irradiance based on satellite observations. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites.
  arXiv  Detail & Related papers  (2021-12-28T03:13:44Z)
• Analysis of False Data Injection Impact on AI based Solar Photovoltaic Power Generation Forecasting [0.0]
  The predictability and stability of forecasting are critical for the full utilization of solar power. This study reviews and evaluates various machine learning-based models for solar PV power generation forecasting using a public dataset.
  arXiv  Detail & Related papers  (2021-10-12T01:44:17Z)
• Principal Component Density Estimation for Scenario Generation Using Normalizing Flows [62.997667081978825]
  We propose a dimensionality-reducing flow layer based on the linear principal component analysis (PCA) that sets up the normalizing flow in a lower-dimensional space. We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
  arXiv  Detail & Related papers  (2021-04-21T08:42:54Z)
• 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)
• 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.