Current solar energy systems design methods mainly rely on experts developing designs on 2D flat screens using outdated CAD models. Immersive 3D design methods may democratise the design process, such that systems can be designed quickly and accurately. Therefore, in this manuscript we measure user engagement or stress levels in both a 2D and 3D immersive virtual reality environment during a solar energy systems design task. User engagement was measured by estimating a user's vital signs using a non-invasive FMCW radar. In our pilot study, four participants tried a 2D and 3D interface while their vital signs were being monitored. According to participant feedback from self-reported questionnaires, our results clearly indicate that the 3D virtual reality offers higher user engagement. These findings could have a tremendous impact on the way we develop renewable energy systems of the future.

Wireless Sensor Network (WSN) nodes rely on batteries that are hazardous and need constant replacement. Therefore, we propose WSNs with solar energy harvesters that scavenge energy from the Sun. The key issue with these harvesters is that solar energy is intermittent. Consequently, we propose machine learning (ML) algorithms that enable WSN nodes to accurately predict the amount of solar irradiance, so that the node can intelligently manage its own energy. Our ML models were based on historical weather datasets from California (USA) and Delhi (India) for the period between 2010 to 2020. In addition, we performed data pre-processing, followed by feature engineering, identification of outliers and grid search to determine the most optimized ML model. In comparison with the linear regression model, the support vector regression (SVR) model showed accurate forecasting of solar irradiance. Moreover, it was also found that the models with time duration of 1 year and 1 month has much better forecasting results than 10 years and 1 week, with both root square mean error (RMSE) and mean absolute error (MAE) less than 7% for