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1. What drives our behaviors in buildings? A review on occupant interactions with building systems from the lens of behavioral theories

   https://doi.org/10.1016/j.buildenv.2020.106928  

   Arsalan Heydarian, Claire McIlvennie (July 2020, Building and environment)

   Occupant behavior has a significant impact on building systems’ operations and efficiency. As a result, several innovative approaches have been introduced to quantify the dynamics of occupants within indoor environments, such as interactions with different building systems and the impact of various feedback and interventions to reduce the building energy consumption. To achieve this, researchers have highlighted the importance of reducing energy consumption without impacting occupant comfort. As a result, there is an increasing body of research evaluating how different theories of behavior across a variety of disciplines can explain occupant interactions with building systems. Future progress in this area calls for an in-depth understanding of behavioral theories in explaining occupant interactions with different building systems. In this paper, we have used a structured literature review approach to investigate how different psychological, sociological, and economic theories have been applied to explain occupant interactions with heating and cooling (HVAC systems), opening windows and ventilation, lighting and shading, electronic appliances, domestic hot water, as well as energy conservation behaviors. Throughout the paper, we identify the most common theories and methodologies applied within the existing research, general findings related to how occupants interact with different building systems, as well as a number of identified gaps within the literature. Finally, we provide a discussion on directions for future research studies in this area under each building system. 

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2. Computationally Efficient Approach for Evaluating Eco-Approach and Departure for Heavy-Duty Trucks

   https://doi.org/10.1177/03611981241254112  

   Wei, Zhensong; Hao, Peng; Kailas, Aravind; Amar, Pascal; Palmeter, Kyle; Levin, Lennard; Orens, Stephen; Barth, Matthew; Boriboonsomsin, Kanok (December 2024, Transportation Research Record: Journal of the Transportation Research Board)

   Connected vehicle-based eco-driving applications have emerged as effective tools for improving energy efficiency and environmental sustainability in the transportation system. Previous research mainly focused on vehicle-level or link-level technology development and assessment using real-world field tests or traffic microsimulation models. There is still high uncertainty in understanding and predicting the impact of these connected eco-driving applications when they are implemented on a large scale. In this paper, a computationally efficient and practically feasible methodology is proposed to estimate the potential energy savings from one eco-driving application for heavy-duty trucks named Eco-Approach and Departure (EAD). The proposed methodology enables corridor-level or road network–level energy saving estimates using only road length, speed limit, and travel time at each intersection as inputs. This technique was validated using EAD performance data from traffic microsimulation models of four trucking corridors in Carson, California; the estimates of energy savings using the proposed methodology were around 1% average error. The validated models were subsequently applied to estimate potential energy savings from EAD along truck routes in Carson. The results show that the potential energy savings vary by corridor, ranging from 1% to 25% with an average of 14%. 

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3. Budget-Constrained Sizing of Renewable and Energy Storage Systems for Farm-Scale Ammonia Production Within the Food-Energy-Water Nexus

   Xuebo Liu; Modarresi, Alex; Kiboma, Lawryn; Wu, Hongyu; Symons, John; Hill, Mary (April 2024, 2024 IEEE Green Technologies Conference)

   Roy A. McCann (Ed.)

   AbstractÐIn the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing clean hybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation. 

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4. A Deep Reinforcement Learning Based Recommender System for Occupant-Driven Energy Optimization in Commercial Buildings

   https://doi.org/10.1109/JIOT.2020.2974848  

   Wei, Peter; Xia, Stephen; Chen, Runfeng; Qian, Jingyi; Li, Chong; Jiang, Xiaofan (February 2020, IEEE Internet of Things Journal)

   In this work, we present recEnergy, a recommender system for reducing energy consumption in commercial buildings with human-in-the-loop. We formulate the building energy optimization problem as a Markov Decision Process, show how deep reinforcement learning can be used to learn energy saving recommendations, and effectively engage occupants in energy-saving actions. is a recommender system that learns actions with high energy saving potential, actively distribute recommendations to occupants in a commercial building, and utilize feedback from the occupants to learn better energy saving recommendations. Over a four week user study, four different types of energy saving recommendations were trained and learned. improves building energy reduction from a baseline saving (passive-only strategy) of 19% to 26%. 

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5. Energy-Aware Resource Management in Vehicular Edge Computing Systems

   https://doi.org/10.1109/IC2E48712.2020.00012  

   Bahreini, Tayebeh; Brocanelli, Marco; Grosu, Daniel (April 2020, Proc. of the IEEE International Conference on Cloud Engineering (IC2E 2020))

   The low-latency requirements of connected electric vehicles and their increasing computing needs have led to the necessity to move computational nodes from the cloud data centers to edge nodes such as road-side units (RSU). However, offloading the workload of all the vehicles to RSUs may not scale well to an increasing number of vehicles and workloads. To solve this problem, computing nodes can be installed directly on the smart vehicles, so that each vehicle can execute the heavy workload locally, thus forming a vehicular edge computing system. On the other hand, these computational nodes may drain a considerable amount of energy in electric vehicles. It is therefore important to manage the resources of connected electric vehicles to minimize their energy consumption. In this paper, we propose an algorithm that manages the computing nodes of connected electric vehicles for minimized energy consumption. The algorithm achieves energy savings for connected electric vehicles by exploiting the discrete settings of computational power for various performance levels. We evaluate the proposed algorithm and show that it considerably reduces the vehicles' computational energy consumption compared to state-of-the-art baselines. Specifically, our algorithm achieves 15-85% energy savings compared to a baseline that executes workload locally and an average of 51% energy savings compared to a baseline that offloads vehicles' workloads only to RSUs. 

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