Introduction

The Smart Campus Energy Lab (SCEL) was created sometime in 2010 with the intent of tackling the task of creating a low-cost, self sustaining meteorological sensor platform. Initially a project of Justin Carland, that particular project has become one of the largest projects that SCEL has.

SCEL joined the Vertically Integrated Projects (VIP) program in Fall 2015.

The Smart Campus Energy Lab at UH Manoa is a student-led lab that strives to be a welcoming and open place of learning for undergraduate and graduate students alike, fostering and developing technical skills such as software engineering, circuit design and machine learning. In addition to technical skills, SCEL also focuses on developing “soft” skills such as communication, teamwork, project management and leadership.

These are the core pillars of the lab:

1. All procedures and projects will be open for the public to see.
2. All hardware and software projects will be open sourced
3. Students will be given the encouragement and opportunity to mentor others

Currently, this is the largest and most active project under our lab.

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.

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 analysis data from the weatherbox to make algorithms to help optimize the weatherbox.

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 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.