Solar Power Prediction Using Machine Learning

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.Comment: 7 page

Genome-Wide Association Analysis of Radiation Resistance in <i>Drosophila melanogaster</i>

<div><p>Background</p><p>Ionizing radiation is genotoxic to cells. Healthy tissue toxicity in patients and radiation resistance in tumors present common clinical challenges in delivering effective radiation therapies. Radiation response is a complex, polygenic trait with unknown genetic determinants. The <i>Drosophila</i> Genetic Reference Panel (DGRP) provides a model to investigate the genetics of natural variation for sensitivity to radiation.</p><p>Methods and Findings</p><p>Radiation response was quantified in 154 inbred DGRP lines, among which 92 radiosensitive lines and 62 radioresistant lines were classified as controls and cases, respectively. A case-control genome-wide association screen for radioresistance was performed. There are 32 single nucleotide polymorphisms (SNPs) associated with radio resistance at a nominal <i>p</i><10⁻⁵; all had modest effect sizes and were common variants with the minor allele frequency >5%. All the genes implicated by those SNP hits were novel, many without a known role in radiation resistance and some with unknown function. Variants in known DNA damage and repair genes associated with radiation response were below the significance threshold of <i>p</i><10⁻⁵ and were not present among the significant hits. No SNP met the genome-wide significance threshold (<i>p</i> = 1.49×10⁻⁷), indicating a necessity for a larger sample size.</p><p>Conclusions</p><p>Several genes not previously associated with variation in radiation resistance were identified. These genes, especially the ones with human homologs, form the basis for exploring new pathways involved in radiation resistance in novel functional studies. An improved DGRP model with a sample size of at least 265 lines and ideally up to 793 lines is recommended for future studies of complex traits.</p></div

Odds ratio histogram of top 32 SNPs at p<10⁻⁵.

<p>Odds ratio histogram of top 32 SNPs at p<10⁻⁵.</p

Association analyses of radiation resistance among 154 DGRP lines.

<p>(A) Quantile-quantile plot. The red line indicates the expected and the black line the observed <i>p</i> values. (B) Manhattan plot of <i>p</i> values. The red dashed line indicates <i>p</i><10⁻⁵.</p

DGRP radioresistance is heritable.

<p>Reciprocal crosses between a completely sensitive RAL-28 and a highly resistant RAL-69 lines were set up to generate F1, which were then selfed to produce F2. 50 males from F1 and F2 of each cross were scored for survival after 1382 Gy irradiation. The data shown represents the mean of two independent trials.</p

Variants in DNA damage and repair genes are not among the top associated SNPs.

<p>A scatterplot of <i>p</i> values for all 10,916 SNPs representing a comprehensive set of 102 DNA damage and repair genes (dots), along with the <i>p</i> values of top 32 SNPs (crosses).</p