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Control and automation systems are desired in today’s fast-paced world as they provide users the luxury of accessing and controlling devices remotely and automate their activities to an extent. This eventually produces desired results such as efficient energy and resource management. In this study a control and automation system was developed, specifically a smart air conditioning system guided by room occupancy accounting. The typical architecture of a home automation system was used as the system’s architecture, wherein different inherent and non-inherently smart devices were integrated through the Internet IoT platform OpenHAB and MQTT as communication protocol. The development of this study served as demonstration of control and automation systems’ capability to be scaled to larger paradigms such as smart buildings and facilitate efficient energy and resource management on a larger scale.

The persistent electricity price hikes in the Philippines and the impact of climate change on the country’s energy demands adversely affect the consumer’s finances on a regular basis. These form the foundation for energy efficiency initiatives in building-based electricity consumption. Such initiatives encompass a wide range of innovations from energy generation to real time monitoring, management and control. At its core, this study supplements institutional efforts on energy management through the electricity consumption monitoring system. A non-intrusive data acquisition system that monitors the aggregate electricity consumption and visualizes the appliance usage patterns in a building setting was developed. This Non-Intrusive Load Monitoring (NILM) technique allowed acquisition of data from a single point of measurement using only a single sensor clamped to the main powerline. The acquired data were streamlined to communicate with the IoT OpenHAB framework via Message Queuing Telemetry Transport (MQTT) protocol and were implemented through deployment. Lastly, Uniform Manifold Approximation and Projection (UMAP) was used for dimension reduction. UMAP was applied to the raw time series data of the aggregate power consumption in order to visualize and determine appliance usage patterns, and effectively label data instances through a scatter plot.