Currently this is the largest and most active project under SCEL. The objective of this project is to design and develop low-cost, accurate, and reliable environmental sensor modules that can easily be reproduced for mass deployment on rooftops across the University of Hawaii at Manoa campus. The meteorological data collected from these modules will assist in planning future renewable energy installations as well as providing risk mitigation for electricity generation through the development of renewable resource prediction and forecasting algorithms.
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Forecasting is predicting the future based on previous and present data and trends in data. Using machine learning techniques such as linearization and classification, we analyze data from the weatherbox to make algorithms to help optimize the weatherbox.
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The objective of the wind sensor project is to build a wind sensor that is low cost, small, reliable, durable, and has no moving parts. The current model is the codenamed “Kiwi” Passive Acoustic Anemometer, which is built using microphones. We want our sensor to detect wind speed and direction in 2D. Once a complete, working model is built, it will be integrated to the weatherbox. The data from the wind sensor can be used for predicting where buildings can be built with natural ventilation.
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