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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. [ABSTRACT FROM AUTHOR]

Active chilled beams (ACB) are gaining popularity worldwide as a potentially energy-efficient air-conditioning technology for buildings. However, the control of ACB system is challenging, as it needs to handle multiple cooling coils and the relatively slow response to cooling load dynamics. This paper reports the implementation of a model predictive control (MPC) system for an ACB system, which employs a linear white-box building model for building energy and indoor condition predictions. A multiple-objectives function is employed in the MPC controller to optimize the energy efficiency in air-conditioning system and indoor thermal comfort while fulfilling the constraints of indoor comfort range (−0.5 < PMV < 0.5). The MPC controller was implemented in the BCA SkyLab test facility in Singapore. The control characteristics and performance of the MPC controller in controlling a conventional fan coil unit (FCU) and an ACB system were investigated experimentally. A conventional feedback control based building management system (BMS) originally installed in the SkyLab test facility was employed as a reference for comparison with the MPC controller. Compared to the original BMS, the MPC controller achieved 14.7% and 20% electricity savings for the conventional FCU and ACB systems, respectively. The MPC controller also kept the indoor PMV within acceptable thermal comfort range at all time. This study demonstrated the real-time multi-objectives optimization capability of the MPC controller in enhancing the performance of ACMV systems. [ABSTRACT FROM AUTHOR]