A Machine Learning Based Approach for Energy Consumption Forecasting in K-12 Schools

Energy costs are the second highest operational expense for K-12 schools in the United States. Improving energy efficiency and moving towards sustainable school buildings not only result in substantial cost savings and reduction of environmental emissions, but also provides an opportunity to enhance students’ awareness regarding energy, environment, and sustainability. Effective tools and techniques that provide thorough understanding of energy consumption in school buildings are valuable to school districts by helping them with prioritizing energy efficiency projects. In the present paper, a multi-layer perceptron (MLP) neural network model is developed for estimating monthly energy consumption of K-12 schools in Brevard County, Florida. The inputs to the network are considered as number of occupants, days of operation per months, building’s area, average monthly outdoor dry-bulb temperature and relative humidity, as well as the month’s number and the output from the network is monthly energy consumption. Various network topologies are considered and tested to achieve the optimal configuration for the network. The selected network is successfully trained using three years of energy consumption data for 25 schools in Brevard County, FL (high schools, middle schools, and elementary schools). The results showed that the developed neural network model is capable of accurate estimation of monthly energy consumption of schools. The network tested and validated using the data from schools which were not included in the training dataset and the errors between the known values and estimated values for monthly energy consumptions are evaluated and discussed. Although the current study covers one particular school district (Brevard county) in a given climate zone (2a-hot and humid), the developed approach can be extended to incorporate various climate zones and serve as an effective tool for school energy conservation managers. The end user may obtain a clear idea of the energy consumption of the school building and how it compares against other buildings within the same category and climate zone, with minimum input data required.

Utilizing Citizen Science as a Tool for Muck Mapping in the Indian River Lagoon

Muck thickness measurements are necessary for guiding muck dredging projects within the Indian River Lagoon (IRL), a coastal estuary that covers one third of Florida's east coast. Marine Resources Council, Florida Institute of Technology, and Brevard County Natural Resources Management successfully engaged Brevard County citizens in the Muck Finders program, a muck research initiative that was designed to help quantify and prioritize muck management actions in the IRL. This program utilizes citizen science as a cost-effective means to expedite the collection of scientifically valid data. Volunteer citizen scientists are given the education, training, and equipment needed to collect scientifically valid data to verify the presence and depth of muck at various locations within the IRL. In 2017, citizen scientists of this program contributed over 300 volunteer hours, collecting muck thickness data at over 750 individual sites. A statistical comparison of muck thickness measurements to duplicate measurements collected by volunteer citizen scientists has confirmed method reliability. Muck thickness maps created from this scientifically valid data aid regulatory agencies to identifying areas where muck dredging will have the most impact.

Recreational Boating Characterization of Brevard County

TP-160 is a 129-page illustrated report by Charles Sidman, Tim Fik, Robert Swett, Bill Sargent, James Fletcher, Susan Fann, David Fann, and Alisa Coffin. It documents the methods, procedures, and results of a map-based mail survey that was distributed to Brevard County boaters to obtain seasonal information about their boating preferences, use profiles, and travel patterns. Published by the UF Sea Grant College Program, September 2007.