Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

Mari R Tye,Christina Kalb,Eric Gilleland,Sue Ellen Haupt,Tara Jensen

doi:10.3390/en12234409

Abstract

With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite mixture distributions. A statistically-objective classification of the overall sky condition may lead to improved forecasts. Combining information from the synoptic driving conditions for daily variability with local processes controlling subdaily fluctuations could assist with forecast validation and enhancement where few observations are available. Gaussian mixture models provide a statistical learning approach to automatically identify prevalent sky conditions (clear, semi-cloudy, and cloudy) and explore associated weather patterns. Here a first stage in the development of such a model is presented: examining whether there is sufficient information in the large-scale environment to identify days with clear, semi-cloudy, or cloudy conditions. A three-component Gaussian distribution is developed that reproduces the observed multimodal peaks in sky clearness indices, and their temporal distribution. Posterior probabilities from the fitted mixture distributions are used to identify periods of clear, partially-cloudy, and cloudy skies. Composites of low-level (850 hPa) humidity and winds for each of the mixture components reveal three patterns associated with the typical synoptic conditions governing the sky clarity, and hence, potential solar power.

Highlights

Improving forecasts of power from solar panels, whether short-range forecasts out to a few hours, or longer, such as subseasonal forecasts, has been the subject of much research in recent years
Solar power can be estimated as a function of solar irradiance, the solar photovoltaic cell properties, and temperature (e.g., [7,8]), allowing power forecasts to leverage weather forecasting of sky conditions
We suggest that a better forecast may be achievable by combining knowledge of both daily and subdaily solar irradiance fluctuations to allow for both the synoptic driving conditions for daily variability, and microscale processes, e.g., governing convective cloud development [40]

Summary

Introduction

Improving forecasts of power from solar panels, whether short-range forecasts out to a few hours, or longer, such as subseasonal forecasts, has been the subject of much research in recent years (as reviewed by [1,2,3,4]). While statistical techniques are often favored for very short-term modeling, satellite-based forecasts of cloud advection perform well in the multihour range [9,10], and longer-term forecasts are generally found to be most reliable from numerical weather prediction models (e.g., WRF Solar [11]). A thorough review of the common forecast approaches at different time aggregations is provided by [4], finding that almost 75% of methods are statistics based. Deterministic irradiance forecasts often combine clustering analyses to classify the sky state [12,13,14,15], followed by a machine-based learning algorithm to develop forecasts [16,17,18,19]. Approaches that combine clustering with machine learning can Energies 2019, 12, 4409; doi:10.3390/en12234409 www.mdpi.com/journal/energies

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