Predicting Solar Array Output Using Weather Sensor Data

With the recent push for renewable energy sources, solar energy is one that is readily available. This paper will explore how to predict the power output of a solar array based on weather data, collected from sensors throughout each day.. With the predicted output of a solar array, a grid operator could decide which other sources of energy would be needed to run to meet energy demand. A linear regression machine learning model will be used to create a correlation between weather data such as irradiation and power output. The best model, the Gradient Boosting Regressor, produced a mean average error of just under 300w.

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Modeling The Impact of Electric Vehicle Adoption on NO2 Levels Using Machine Learning: A Predictive Analysis

Air pollution, notably nitrogen dioxide (NO2), poses severe health and environmental risks. The research question explores whether increasing EVs adoption can visibly reduce NO2 levels.