Forecasting and Uncertainty Quantification for Renewable Energy Production Using Bayesian Spatio-Temporal Models

Project Abstract

This study presents a comprehensive framework for forecasting renewable energy production and quantifying associated uncertainty through Bayesian spatio-temporal models that integrate meteorological covariates, solar irradiance, wind speed, and plant-level operational data. By leveraging hierarchical Bayesian methods with spatially structured random effects and temporal autoregression, the proposed approach captures nonstationarities across time and space, accommodates irregular observation gaps, and naturally propagates uncertainty from input data to forecasted outputs. The methodology encompasses data preprocessing, feature extraction from meteorological forecasts, and a modular model specification that can adapt to solar, wind, and hybrid energy systems. We implement a spatio-temporal Gaussian process prior over latent production capacity with informative priors derived from historical performance, equipment characteristics, and site-specific climatology. The model incorporates measurement errors, missingness mechanisms, and censoring due to plant outages, while enforcing physical constraints such as capacity limits and diurnal/seasonal cycles. To facilitate real-time decision support, we develop scalable computational strategies, including sparse approximations for Gaussian processes, variational inference for fast posterior updates, and parallelized MCMC sampling for robust posterior characterization. The research evaluates forecast accuracy using probabilistic metrics such as CRPS, PIT histograms, and coverage of predictive intervals, across multiple horizons (intra-day to weekly) and diverse geographic regions. A thorough comparison with benchmark approaches—classical time series models, machine learning ensembles, and non-spatial Bayesian methods—demonstrates improvements in point forecasts and explicit uncertainty quantification under varying meteorological regimes and operational contingencies. We also examine the impact of incorporating covariate information (cloud cover, temperature, humidity, wind shear, and turbine/ PV array efficiency curves) on predictive performance, and quantify the value of spatial borrowing—how information from neighboring sites reduces forecast error in data-sparse regions. The framework addresses risk-sensitive planning for grid integration, storage optimization, and dispatch strategies by propagating predictive uncertainty into optimization objectives and constraints. Case studies cover solar PV and onshore wind farms across temperate and arid climates, including scenarios with renewable intermittency spikes and sudden weather perturbations. Results indicate that Bayesian spatio-temporal models yield superior calibration and sharper predictive intervals, particularly for multi-site aggregations and longer horizons, while maintaining computational tractability suitable for operational deployment. Sensitivity analyses reveal robustness to prior misspecification and missing data patterns, guiding practitioners on data requirements and prior elicitation. The work contributes a unified probabilistic forecasting pipeline that seamlessly integrates physical, statistical, and operational considerations, enabling more reliable energy production forecasts, improved uncertainty management, and informed decision-making for renewable energy management and grid stability.

Project Overview

What This Project Is About

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Plain-language overview of forecasting energy production and understanding the uncertainty in those predictions. The project uses simple ideas about time and space to predict how much solar or wind energy will be produced and how sure we are about those predictions.

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The Problem It Addresses

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Renewable energy output varies with weather and location, making planning and grid management hard. Existing methods may ignore where and when conditions change, leading to less reliable forecasts and wasted resources.

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Objectives of the Project

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1. Explain the main ideas behind forecasting energy with basic, non-technical language.
2. Describe how uncertainty can be quantified in simple terms.
3. Show how data from different places and times can be combined to improve predictions.
4. Provide a small, clear example or case study to illustrate the method.
5. Discuss potential benefits for energy planning and policy.

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What You Will Do Step by Step

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1. Learn basic concepts of time-series and spatial thinking in energy data.
2. Collect or access a simple dataset of renewable energy production and weather factors.
3. Apply an approachable modeling idea that ties data across time and space to improve forecasts.
4. Estimate what future production might look like and how sure we are about those numbers.
5. Validate forecasts using simple checks and sister methods.
6. Interpret results in plain language for decision-makers.
7. Prepare a short report summarizing methods, findings, and limitations.

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Expected Outcome

A clear, easy-to-understand forecasting approach with quantified uncertainty that can assist grid operators and policymakers in planning for renewable energy supply.