Implementation of effective and low-cost building monitoring system(BMS) using Raspberry Pi

One of the best companions in today’s modern world from a kid to a higher qualified professional is the Raspberry Pi. This credit card sized microprocessor can perform tons of applications based on the user’s requirements and the benefit through this are endless. According to Ericsson, by 2020, ther...

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Bibliographic Details
Main Authors: Arumuga Perumal, Venkat Subramanian, Baskaran, Krishnamoorthy, Rai, Suleman Khalid
Other Authors: Energy Research Institute @ NTU (ERI@N)
Format: Article
Language:English
Published: 2018
Subjects:
Online Access:https://hdl.handle.net/10356/89284
http://hdl.handle.net/10220/46190
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Institution: Nanyang Technological University
Language: English
Description
Summary:One of the best companions in today’s modern world from a kid to a higher qualified professional is the Raspberry Pi. This credit card sized microprocessor can perform tons of applications based on the user’s requirements and the benefit through this are endless. According to Ericsson, by 2020, there will be 50 billion connected devices which is seven times the total world population. So the acquisition and utilization of such data from the environment and building Energy consumption for creating a better Monitoring System will always be a challenge in the area of Internet of Things (IoT) and big data analytics. So it is very much important to implement an effective and low cost Building Monitoring System to understand more about the building environment and energy utilization in order to take automatic adaptive decisions for better Energy consumption in the upcoming future. The overall objective of this paper is to utilize Raspberry Pi as a Key component of Building Monitoring System to Monitor the environmental parameters viz., Temperature, Barometric Pressure, humidity, Light etc and also to monitor the power consumption of the building environment in order to develop an effective environmental monitoring system which can be properly utilized in performing data analytics for Energy harvesting in the future.