This paper presents linear regression models to predict the daily energy production of three photovoltaic (PV) systems located in southeast Virginia. The prediction is based on daylight duration, sky index, the average relative humidity, and the presence of fog or mist. No other daily weather report components were statistically significant. The proposed method is easy to implement, and it can be used in conjunction with other advanced methods in estimating any given future day’s energy production if weather prediction is available. Data from 2013-2015 was used in the model construction. Model validation was performed using newer (2016, 2017, 2020, and 2021) data not used in the model construction. Results show good prediction accuracy for a simple methodology, free of system parameters, that can be utilized by ordinary photovoltaic energy users. The entire data set can be downloaded using the link provided.

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We report the discovery and genome sequences of three FH cluster actinophage infecting Arthrobacter globiformis B2979. Lilmac1015 and Klevey were isolated from riverbank soil and Prairie from soil collected below a tree. Their respective genome lengths are 49,978, 50,075, and 49,392 bp, with 80, 81, and 78 predicted protein-coding genes.

We report the discovery and genome sequence of phage Adumb2043, a siphovirus infecting Arthrobacter globiformis , B2979-SEA. Adumb2043 was isolated from soil collected in Colorado Springs, Colorado. The genome has a length of 43,100 bp and contains 68 predicted protein-coding genes and no tRNA genes. Adumb2043 is related to actinobacteriophages Elezi and London.