Objective framework for optimal distribution of solar irradiance monitoring networks

Abstract

Time-resolved characterization of solar irradiance at the ground level is a critical element in solar energy analysis. Siting of nodes in a network of solar irradiance monitoring stations (MS) is a multi-faceted problem that directly affects the determination of the solar resource and its spatio-temporal variability. The present work proposes an objective framework to optimize the deployment of solar MS over a sub-continental region. There are two main components in the proposed methodology. The first employs cluster analysis using the affinity propagation algorithm, to select the optimal number of clusters (regions with coherent solar microclimates) upon internal coherence criteria. The second component employs stochastic prediction and validation, through the use of a Bayesian maximum entropy method, and selects the optimal MS configuration, according to geostatistical criteria, among the solutions recommended by the cluster analysis. We apply this two-pronged methodology to determine clusters and optimal locations for global horizontal irradiance monitoring across the state of California. In this proof-of-concept study, 3 disparate MS configurations are examined within the cluster partition. The subsequent geostatistical analysis indicates that all configurations rank almost equally well based on different statistical error measures. The optimal configuration can be singled out depending on desired criteria of choice.

Lagged Influence of Fine Particulate Matter and Geographic Disparities on Clinic Visits for Children’s Asthma in Taiwan

“…We used the Bayesian maximum entropy (BME) method to estimate the spatiotemporal distribution of air pollution concentrations and weather conditions for each unmonitored location by day from 2005 to 2010. The BME method is a spatiotemporal interpolation technique to incorporate measurement data, prior knowledge of neighbor information, and local spatiotemporal covariates [ 50 , 51 , 52 , 53 ] and has been applied to estimate the ambient pollution concentration across space–time previously [ 54 , 55 , 56 , 57 ]. The process of spatiotemporal air pollutants can be characterized by spatiotemporal trend and covariance.…”

Intra-hour irradiance forecasting techniques for solar power integration: A review

“…Optimized distribution of monitoring stations not only reduces the required number of monitoring stations but also improves the accuracy and robustness of the spatial forecasts. Zagouras et al (Zagouras et al, 2015a) propose an objective framework to optimize the distribution of solar irradiance monitoring networks in order to facilitate solar forecasts. The basic theory is to identify coherent zones of solar micro-climate for utility scale territory using unsupervised clustering techniques.…”

Annual Daily Irradiance Analysis of Clusters in Mexico by Machine Learning Algorithms

“…Planning the strategic locations for the installation of a comprehensive measurement network proves to be indispensable for the assessment of solar resources, especially in vast territories characterized by a multitude of climatic variations [1,2]. The substantial financial commitment entailed in establishing solar radiation measurement stations, coupled with the inherent challenges associated with their sustained maintenance, underscores the critical need for methodological approaches capable of precisely determining both the optimal number of stations and the most suitable deployment sites [3].…”