In-Network Distributed Solar Current Prediction

Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this paper, we present a model and algorithms for distributed solar current prediction, based on multiple linear regression to predict future solar current based on local, in-situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-week-long experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10^{-7}% of the harvested energy) to gain a prediction improvement of 39.7%.Comment: 28 pages, accepted at TOSN and awaiting publicatio

Statistical Analysis of the Global Solar Radiation in Cúcuta using the ANOVA Model

Objective: This paper presents a statistical analysis of solar radiation in the city of Cúcuta, aiming to provide a detailed description of its variability between 2005 and 2015. This information represents an assessment tool to study the solar potential of the region for photovoltaic system design, motivated by the need to improve the cost-effectiveness of this technology, and thus increase its penetration in the Colombian electric grid. Methodology: Three weather databases with hourly data were studied, from which the one with the largest amount of data available was selected. By means of the R Studio software, two types of statistical methods were executed: single factor variance analysis (ANOVA) and Bonferroni test. From this, graphs representing the statistical summary of solar radiation values in the last decade were obtained. Results: The ANOVA showed a p-value of 6,28x10-7, indicating that there is a statistically significant difference in the sample mean between the different years of study. Likewise, the years and months with the greatest deviation and the possible causes for the variability of this parameter were identified. Conclusions: Despite showing a stable behavior, the radiation of the city of Cúcuta requires a very specific analysis for its use in applications that need a high sensitivity in the handling of this information, since there are statistically significant variations that can occur for its use. Funding: Universidad Francisco de Paula Santander        Objetivo: Esta investigación presenta un análisis estadístico de la radiación solar en la ciudad de Cúcuta, con el objetivo de brindar una descripción detallada de su variabilidad entre los años 2005 y 2015. Esta información representa una herramienta evaluativa en el estudio del potencial solar de la región para el diseño de sistemas fotovoltaicos, partiendo de la necesidad de mejorar la relación costo/beneficio de esta tecnología, y así incrementar su penetración en la matriz eléctrica colombiana. Metodología: Se realizó un estudio de tres bases de datos climatológicas con información horaria, seleccionando aquella con la mayor cantidad de datos disponibles. Por medio del software R Studio, se ejecutaron dos tipos de métodos estadísticos: análisis de la varianza de un solo factor (ANOVA) y test de Bonferroni. A partir de esto, se obtuvieron gráficas que representan el resumen estadístico de los valores de radiación solar en la última década. Resultados: El análisis ANOVA arrojó un valor p de 6,28x10-7, indicando que existe una diferencia estadísticamente significativa de la media muestral entre los diferentes años de estudio. Asimismo, se identificaron los años y meses con mayor desviación y las posibles causas de la variabilidad de este parámetro. Conclusiones: A pesar de tener un comportamiento estable, la radiación de la ciudad de Cúcuta requiere de un análisis muy específico para su uso en aplicaciones que necesiten una alta sensibilidad en el manejo de esta información, ya que hay variaciones estadísticamente significativas que se pueden presentar para su uso. Financiamiento: Universidad Francisco de Paula Santander.