Green Planning of IoT Home Automation Workflows in Smart Buildings

Abstract

The advancement of renewable energy infrastructure in smart buildings (e.g., photovoltaic) has highlighted the importance of energy self-consumption by energy-demanding IoT-enabled devices (e.g., heating/cooling, electromobility, appliances), which refers to the process of intelligently consuming energy at the time it is available. This stabilizes the energy grid, minimizes energy dissipation on power lines but more importantly is good for the environment as energy from fossil sources with a high CO 2 footprint is minimized. On the other hand, user comfort levels expressed in the form of Rule Automation Workflows (RAW) , are usually not aligned with renewable production patterns. In this work, we propose an innovative framework, coined IoT Meta-Control Firewall (IMCF + ) , which aims to bridge this gap and balance the trade-off between comfort, energy consumption and CO 2 emissions. The IMCF + framework incorporates an innovative Green Planner (GP) algorithm, which is an AI-inspired algorithm that schedules energy consumption with a variety of amortization strategies. We have implemented IMCF + and GP as part of a complete IoT ecosystem in openHAB and our extensive evaluation shows that we achieve a CO 2 reduction of 45%-59% to satisfy the comfort of a variety of user groups with only a moderate ≈ 3% in reducing their comfort levels.

PIR Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

“…They reviewed many studies on how to count people in different places, noting the types of spaces, their sizes, and how these studies measured results. They used this information to create a system for classifying the number of occupants in their own study, as follows: [0], [1][2], [3][4], [5][6], [7][8][9], [10][11][12], [13][14][15], [16][17][18], [19][20][21][22],. .…”

Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches

“…They reviewed many studies on how to count people in different places, noting the types of spaces, their sizes, and how these studies measured results. They used this information to create a system for classifying the number of occupants in their own study, as follows: [0], [1][2], [3][4], [5][6], [7][8][9], [10][11][12], [13][14][15], [16][17][18], [19][20][21][22],. .…”

“…AI integration also makes predictive maintenance easier, allowing for the early detection and resolution of possible problems to ensure the longevity and effectiveness of building systems [ 14 , 15 , 16 , 17 ]. Essentially, smart buildings offer a sustainable, efficient, and user-centric environment that combines the capabilities of the IoT and AI [ 18 , 19 ]. In smart buildings, occupancy information is critical for shaping energy consumption, optimizing HVAC and lighting systems for energy efficiency, improving occupant comfort through personalized environments, strengthening security by detecting unexpected occupancy in real time, and making design adjustments based on space utilization patterns.…”

AI-based fog and edge computing: A systematic review, taxonomy and future directions

“…Li et al [75] present a human pose forecasting system for smart homes leveraging graph convolutional neural network on the IoT edge for online learning. IoT Meta-Control Firewall (IMCF+) is proposed to mitigate energy consumption and CO2 emission issues while also maintaining user comfort [76].…”