Buildings are responsible for a significant portion of energy consumption in the US, accounting for more than 40% of US primary energy consumption. Heating, ventilation and air-conditioning (HVAC) accounts for nearly 50% of that use. Conditioning buildings is important since people spend 87% of their time in the place they live (residential) and the place they work (commercial). Despite this massive expense, many users are dissatisfied with the thermal conditions in buildings. Savings made in HVAC systems, therefore, have a major impact on energy consumption and cost, together with the reduction of greenhouse emissions for the nation. Equally critical is to provide thermal quality of service to their users, so people are comfortable in the place they reside and work.In this paper, we explore the tradeoff between commercial building HVAC energy consumption and the quality of thermal conditioning provided to users. We argue that optimal HVAC control cannot be achieved due to lack of critical information, namely where the users are inside the building, what do they want with respect to thermal comfort and how each zone responds to thermal changes. In this work, we present OFFICE, a model predictive control (MPC) framework for smart building HVAC control. The framework has several components that help to address the current HVAC control systems shortcomings, including (a) occupancy sensing in real-time, (b) occupancy prediction models based on historical occupancy data, (c) human-in-the-loop comfort feedback, (d) data-driven thermodynamic building models, and (e) weather forecasting data. All these components provide the necessary input to our model predictive control optimization framework that minimizes monetary costs in energy use while maintaining quality comfort bounds for the building’s users based on real-time user’s feedback. We developed a large system that involves all the above components, replacing the Building Management System control algorithms, taking over full control of the HVAC system. We tested OFFICE in a real LEED Gold certified university building with over 20 workers performing their daily tasks for 4 weeks, and we showed that we could obtain monetary costs savings of more than 10% while at the same time reducing the users’ dissatisfaction levels with thermal comfort from 25% to 0% dissatisfaction, significantly improving the quality of thermal service provided to the building’s users.

Lawns, also known as turf, cover an estimated 128,000 km 2 in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain. With this consumption comes a large amount of environmental, economic, and social incentive to make turf irrigation systems as efficient as possible. Recent work introduced the concept of distributed control in irrigation systems, but existing control strategies either do not take advantage of the distributed control, or do not revise the strategy over time in response to collected data. In this work, we introduce OPTICS, a data-driven control strategy that self-improves over time, adapts to the local specific conditions and weather changes, and requires virtually no human input in both setup and maintenance providing a plug-and-play system that requires minimal pre-deployment efforts. In addition to substantial improvements in ease-of-use, we find across 4 weeks of large-scale irrigation system deployment that OPTICS improves system efficiency by 12.0% in comparison to industry best and 3.3% in comparison to academic state of the art. Despite using less water, OPTICS also was found to improve quality of service by a factor of 4.0× compared to industry best and 2.5× compared to academic state of the art.

As lawn irrigation is estimated to consume 7 billion gallons of scarce fresh water each day in North America alone, lawn irrigation systems are a high priority for improvements in efficiency. To this end, recent work has introduced several key advancements in irrigation control. Distributed actuation systems allow the irrigation system to apply water completely independently across the field allowing flexibility of control, and the use of fluid flow modeling and optimization allows more efficient schedules to be computed automatically, significantly improving the irrigation quality of service as well. However, the proposed systems are designed with centralized architectures that introduce single points of failure, computational bottlenecks in data processing, and significant network energy for data forwarding used by the centralized data-driven modeling strategies. In response to these challenges, we propose and demonstrate WISDOM, whose novel and flexible hardware and processing pipelines enable the use of a distributed system for the management of irrigation systems of any scale, with energy independence by way of energy harvesting. Across 4 weeks of live system deployment, we find that the WISDOM system can save up to 32.9% of water in comparison to industry-best, while maintaining a perfect quality of service to the plant. Furthermore, with substantial analysis in simulation we find that in addition to practical system improvements, the use of the proposed distributed system within typical operating conditions will provide all of the efficiency and quality-of-service benefits of the globally-modeled, centrally controlled systems, while allowing the robust control of irrigation systems of any size.

Lawns make up the largest irrigated crop by surface area in North America and carry with it a demand for over 7B gallons of freshwater each day. Despite recent developments in irrigation control and sprinkler technology, state-of-the-art irrigation systems do nothing to compensate for areas of turf with heterogeneous water needs. In this work, we overcome the physical limitations of the traditional irrigation system with the development of a sprinkler node that can sense the local soil moisture, communicate wirelessly, and actuate its own sprinkler based on a centrally computed schedule. A model is then developed to compute moisture movement from runoff, absorption, and diffusion. Integrated with an optimization framework, optimal valve scheduling can be found for each sprinkler node in the space. In a turf area covering over 10,000ft 2 , two separate deployments with four weeks of fine-grained data collection show that DICTUM can reduce water consumption by 23.4% over traditional campus scheduling, and by 12.3% over state-of-the-art evapotranspiration systems while substantially improving conditions for plant health. In addition to environmental, social, and health benefits, DICTUM is shown to return its investment in 16 to 18 months based on water consumption alone.

Lawns, also known as turf, cover an estimated 128,000km2[9] in North America alone, with landscape requirements representing 30% of freshwater consumed in the residential domain [27]. With this consumption comes a large amount of environmental, economic, and social incentive to make turf irrigation systems as efficient as possible. Recent work introduced the concept of distributed control in irrigation systems, but existing control strategies either do not take advantage of the distributed control, or don't revise the strategy over time in response to collected data. In this work, we introduce PICS, a data-driven control strategy that self-improves over time, adapts to the local specific conditions and weather changes, and requires virtually no human input in both setup and maintenance providing a plug-and-play system that requires minimal pre-deployment efforts. In addition to substantial improvements in ease-of-use, we find across 4 weeks of large-scale irrigation system deployment that PICS improves system efficiency by 12.0% in comparison to industry best and 3.3% in comparison to academic state-of-the-art. Despite using less water, PICS also was found to improve quality of service by a factor of 4.0x compared to industry best and 2.5x compared to academic state of the art.

Humans spend 90% of their lives inside buildings, but often the Heating, Ventilation, and Air Conditioning (HVAC) systems of commercial buildings do not properly maintain occupant comfort. Use of feedback through comfort voting applications has been shown to improve the quality of service, but the effects of application feedback and user interface design has not been investigated. In this work, we present several methods of feedback that use data presentation and environmental interaction in comfort voting applications. Through a 40 week user study of 61 University employees across 3 buildings, we show that feedback systems can be used to increase user satisfaction with thermal conditions from 33.9% to 93.3% and reduce energy consumption up to 18.99% compared to a system without voting. In addition, we find that by including a drifting control strategy, we find energy savings up to 37% can be realized without a significant reduction in satisfaction.

Lawns make up the largest irrigated crop by surface area in North America, and carries with it a demand for over 9 billion gallons of freshwater each day. Despite recent developments in irrigation control and sprinkler technology, state-of-the-art irrigation systems do nothing to compensate for areas of turf with heterogeneous water needs. In this work, we overcome the physical limitations of the traditional irrigation system with the development of a sprinkler node that can sense the local soil moisture, communicate wirelessly, and actuate its own sprinkler based on a centrally- computed schedule. A model is then developed to compute moisture movement from runoff, absorption, and diffusion. Integrated with an optimization framework, optimal valve scheduling can be found for each node in the space. In a turf area covering over 10,000ft2, two separate deployments spanning a total of 7 weeks show that MAGIC can reduce water consumption by 23.4% over traditional campus scheduling, and by 12.3% over state-of-the- art evapotranspiration systems, while substantially improving conditions for plant health. In addition to environmental, social, and health benefits, MAGIC is shown to return its investment in 16-18 months based on water consumption alone.

Lawns, both public and private, make up the largest irrigated crop in North America by surface area. Although there have been improvements in sprinkler head technology and weather assimilation, state-of-the-art irrigation systems do nothing to adjust for heterogeneous terrain or varying lawn environments. In this work, a computationally lightweight soil moisture movement model is developed, which allows the computation of optimal irrigation valve scheduling using standard optimization techniques. A prototype sprinkler head is produced with the ability to sense local soil moisture conditions, wirelessly communicate, and independently actuate based on the optimal schedule centrally computed. This prototype is then deployed to control two parallel irrigation systems covering a total of more than 10,000 ft$^2$ for a duration of 5 weeks. It is shown that lawn health can be maintained by using the topography of the space to take advantage of runoff to provide improved coverage while using an average of 23.4\% less water. We also show that the initial capital and operating costs of our system could be amortized by our water savings in $~$13 months while maintaining and/or improving quality of irrigation and lawn health.

Buildings are often inefficiently conditioned. Rooms that are empty are needlessly conditioned and partially filled rooms are conditioned assuming maximum occupancy. In this demonstration, we describe a system that reduces energy consumption by opportunistically reducing energy consumption based on room usage; we only condition rooms currently occupied and condition the space based on real-time occupancy measurements. We will show how a thermal sensor array can be used measure occupancy in real-time and how this occupancy information can be integrated with a real building management system in order to control the heating, cooling, ventilation and lighting of a building to optimize energy usage.