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Why Big Indoor Ag Needs Big Data

Abstract

The indoor agriculture market has become a promising and growing alternative to traditional agriculture due to its higher yield, shorter crop cycles, and climate resiliency.  It also comes a large cost –indoor agriculture is a major strain on utilities, has a high carbon footprint, and is difficult to monitor effectively given the fact that it’s a complex organic process requiring multiple building systems. In cannabis, the most profitable and fastest growing indoor crop, significant energy requirements are straining the Massachusetts utility grid and will have a significant environmental impact as the industry continues to expand.   By implementing systems that track energy data and operational performance, the indoor agriculture industry will become more energy efficient and profitable, allowing large producers to effectively develop and measure sustainable systems such as lighting, HVAC and building controls.

In this course we will explore:
•         An overview of the indoor agriculture industry and key data on its present state
•         Current technology and practices implemented by industry leaders
•         How Massachusetts regulators have used data to impact the industry
•         The way data will shape the industry and what this means for everyone

Presented By

Morgan Abraham, P.E., C.E.M., LEED AP
Private Consultant

Professional Bio
Morgan is a Senior Electrical Engineer with seven years of experience developing power systems, lighting controls, fire alarm layouts, utility services, and energy models. His previous projects have included cannabis cultivation and processing facilities, data centers, universities, corporate headquarters, high-rise residential and waste water treatment facilities where he has leveraged his experience in energy performance, automation, software architecture, hardware manufacturing, and CAD modeling. Morgan has been a longtime proponent of utilizing parametric data to build models that measure the impacts of design decisions in a project to ensure an optimal outcome. These models have subsequently been instrumental in developing control systems that optimize energy utilization and  performance in operational facilities.