K. Max Zhang

Abstract Retrofitting building stock through heating electrification and energy efficiency improvements is essential for achieving carbon neutrality. Understanding the effects of electrification and efficiency retrofits on building-resident satisfaction and adaptive behaviors is important, as these directly impact retrofitting success, adoption rates, energy consumption, and performance. There is a gap in understanding the combined effects of heating electrification and building efficiency retrofits. Using data collected over 2.5 years, we performed integrated qualitative and quantitative analyses to evaluate the combined effects of heat pump electrification and a roof insulation retrofitting in a 10-unit New York City apartment building. Building-resident satisfaction with each strategy was assessed, and impacts on occupant thermal comfort, energy behavior, indoor thermal environment, and energy consumption were analyzed. Despite perceived challenges and resident skepticism, air source heat pumps (ASHPs) provided adequate indoor thermal comfort. ASHPs were preferred over steam boiler heating for controllability, noise reduction, and improved thermal comfort. Unintended benefits included improved aesthetics, reduced real estate needs, and decreased burn potential. With heat pumps, some residents adopted energy-conservative behaviors while others adopted “comfort-taking” behaviors, prioritizing comfort over conservation. The roof insulation retrofit further improved resident thermal comfort and decreased total building heating energy requirements by 25.3–34.2% and heating peak power requirements by 10.7%. The retrofit also improved ASHP efficiency in previously uninsulated spaces, effectively mitigating heat pump undersizing effects. Combined energy retrofitting strategies could play a key role in ensuring thermal comfort and building energy efficiency toward carbon neutrality.

In this study, a machine-vision-based automatic control system for the topsoil stripping of Fritillaria ussuriensis Maxim. (FUM) was designed to address the problems of manual adjustment, low control accuracy, and response lag in stripping-depth control during FUM harvesting. An improved YOLOv5s-SA target detection algorithm was used to calculate FUM density and was deployed on the Jetson Nano edge-computing platform. Combined with a fuzzy control algorithm, it drives the servo electric cylinder to achieve dynamic depth adjustment of the scraping board. Test results showed that, after deploying the target detection algorithm on the edge AI device and accelerating it with TensorRT, the average inference time was 0.077 s, and the system response time was 0.26 s, meeting the real-time requirements of agricultural operations. Simulation results indicated that the average error between the stripping depth of the automatic control system and the preset depth was 3.72 mm, representing a 44.1% improvement compared with fixed-depth control. The average ideal stripping rate reached 54.96%, an improvement of 21.66% over the 33.3% achieved under fixed-depth control.

Abstract The negative impact of wildfire smoke on solar photovoltaic (PV) generation by reducing the amount of solar irradiance reaching the modules has been observed worldwide. However, the predictive capability to capture the impact on solar electricity production still needs to be improved. For example, in the summer of 2023, smoke from Canadian wildfires spread to the northeastern U.S., impacting solar PV output in the region. The New York Independent System Operator (NYISO) day-ahead forecasts for this period significantly overpredicted PV output. This paper presents novel machine learning-based models for predicting the hourly solar capacity factor, focusing on improving predictive performance during periods of severe wildfire smoke. The results demonstrate a R 2 value of up to 0.85 for the severe wildfire periods (aerosol optical depth (AOD) above the 99.99th percentile) from our models, significantly outperforming NYISO’s R 2 value of 0.50 across six load zones included in the analysis. The greatly enhanced performance arises from two innovations. First, we adopted a series of data products, newly available in the public domain, from the high-resolution rapid refresh smoke (HRRR-Smoke) weather forecasting system. These include predictions of the AOD and the downward shortwave radiation flux incorporating aerosol impacts. Our study marks the first time the HRRR-Smoke wildfire AOD product has been used in solar electricity forecasts. Second, we employed upsampling strategies to address the data imbalance issues due to the inherently infrequent nature of wildfire events. As the data products are publicly available, our methodology can be readily adopted by power system operators to enhance predictions of solar electricity production during periods of wildfire smoke, ensuring the reliability of power grids with high penetration of solar energy.

In this study, through a novel network-based data-driven method, we reveal a likely unintended, nighttime-specific impact of construction activities on elevated coarse particulate matter (PM c ) concentrations in a metropolitan area.

LoRaWAN is a popular Long-Range Low-Power wireless communications protocol that is enabling many IoT applications worldwide, with more networks growing both in size and number around the world. To effectively plan and operate these networks, it is necessary to have tools that reliably quantify, measure, and predict the connection quality provided by LoRaWAN receivers. Being able to reliably quantify connection quality would allow LoRaWAN adopters to answer questions such as, “What does ‘good coverage’ mean?”. Reliably measuring coverage would allow for questions like “What is the quality of network coverage in a given area?”, to be answered, while predicting connection quality would allow adopters to answer questions such as “What would the coverage quality be if we deployed an additional wireless receiver in this location?” This paper proposes a novel data-driven approach to connection quality modeling that is tailored for LoRaWAN with the following features. First, connection quality is quantified by the packet reception rate (PRR), as opposed to the traditional received signal strength typical of generic radio planning tools. The PRR more closely captures what network operators and users ultimately care about. Next, we leverage a large set of original data to fit a model for PRR. This dataset is unique in two ways. First, it includes transmissions that were transmitted but not received by any gateway, eliminating an otherwise persistent source of bias in empirical estimates of wireless connectivity. Second, it includes features derived from high-fidelity terrain topology extracted from LiDAR point clouds. Our model includes both feature extraction and estimation. We evaluate our model out-of-sample, including in regions entirely disjoint from the training data, and show that it is considerably more accurate than common benchmark wireless propagation models. Finally, we demonstrate how our model can be used to provide coverage maps in a real-world network.

In this paper, a stochastic optimal power flow problem that relies on both the supply and demand sides for reserve provision is formulated. The demand side can provide reserves through demand response. A part of the load is assumed to be flexible, and a virtual storage model is utilized to represent the aggregate flexible load. Two sources of uncertainty are considered, namely inaccurate wind power generation forecasts and erroneous ambient temperature forecasts. Chance-constrained constraints are introduced to guarantee feasibility above a certain probability threshold for different realizations of the involved random variables and dealt with via sampling. The developed formulation is tested on the IEEE 9-bus system. The results demonstrate a greatly significant reserve provision potential on the demand side that can be tapped so long as customers are willing to participate in demand response programs. Special attention should be paid to the recovery period of available resources on the demand side, giving priority to careful management of these resources.

Abstract Without proper battery thermal management, electric vehicles (EVs) suffer from significantly reduced efficiency and performance in cold climates, creating a barrier to electrifying the transportation sector. In this study, we have developed a modular, hybrid battery thermal management system that combines phase change material (PCM) with internal heating. This hybrid system uses PCM to store waste heat generated during driving, maintaining the battery temperature during shorter stops between consecutive trips. For longer stops, internal heating can reheat the battery if the latent heat of the PCM has dissipated. Moreover, by applying PCM on the outside, the proposed system is modular, requiring no structural change within the existing battery module and reducing the impact of increased thermal inertia on battery reheating time. Through both laboratory experiments and numerical simulations, we found that the proposed system could hold the battery temperature above 20 °C for around 2 h at an ambient temperature of −15 °C and achieved a battery reheating time (from 0 °C to 20 °C) of only 11 min. By reusing waste heat during short stops, this system can promote EV adoption in cold climates through improved battery efficiency, particularly for EVs making frequent stops, such as taxis and delivery vehicles.

value ranged from 0.47 to 0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable ranges for air quality dispersion modeling. Even though the multi-regime model is parameterized for coniferous trees, our sensitivity study indicates that it can provide useful predictions for hedges/bushes vegetative barriers as well.

Abstract Internet of Things (IoT) sensor networks are an emerging technology at the center of the datafication and optimization of far-reaching environmental infrastructures—from “smart cities” to workplace efficiencies. However, this low-power, low-cost technology is also well suited to local deployments in rural communities, which are often overlooked by digital development initiatives. Therefore, we used a social construction of technology approach to study how various U.S.-based IoT stakeholders—including designers and advocates as well as citizen stakeholders—understand and value sensor network technologies. Through observational methods, in-depth interviews, and participatory design research in a rural Upstate New York municipality, we worked to design sensor networks with rural community members to generate data about and for community members to further local knowledge. We found that designing rural sensor networks requires stakeholders to navigate obstacles of communication about sensors and communication through sensors to facilitate secure, ethical, and localized sensing in rural communities.

A high voltage direct current (HVDC) line is proposed to be built between upstate New York and New York City (NYC), with both ends inside the control area of the New York Independent System Operator, Inc. (NYISO). This is the first fully controllable internal HVDC line in the United States to be modeled in the energy market. This paper proposes a generalized locational marginal price (LMP)-based energy market model for internal controllable HVDC lines (ICL). A key advantage of the proposed model is that the ICL operator can bid competitively in the energy market for power flow in both directions. The proposed model is initially demonstrated with a three-bus test system and then simulated in the NYISO’s day-ahead energy market. A sensitivity analysis of ICL bids is conducted, and ICL flow’s impact on market conditions is illustrated. The simulation results show the proposed model can effectively optimize the ICL schedule for cost-saving and congestion relief.

Large-scale solar energy development is best suited for flat, open areas, making agricultural land a prime target, particularly in regions dominated by agricultural activity such as New York State (NYS). In order to preserve prime farmland, it is imperative to develop effective policies to maximize the potential of deploying solar farms on marginal, less valuable lands, especially as a growing number of brownfields, and more specifically landfills, remain underutilized in NYS. The concept of repurposing contaminated land for solar energy, known as Brownfields to Brightfields (B2B), has shown promising results in case studies across the United States, but major barriers prevent widespread industry adoption and implementation, including scale limitations and infrastructure integration challenges. This study uses Geographic Information System (GIS)-based tools to demonstrate potential for inactive landfill solar redevelopment in NYS, incorporating important considerations for solar siting and evaluating four possible future infrastructure expansion scenarios. Results indicate that 55 - 67 % of inactive landfill area in NYS demonstrate medium to good suitability for solar development in the four scenarios analyzed, and landfill redevelopment can contribute up to 8.7 % of anticipated solar capacity in 2050 needed to reach statewide climate goals. These findings show the potential for inactive landfills to play a major role in decarbonizing the grid while preserving prime farmland in NYS.

Agrivoltaics, the co-location of solar and food production, is a promising solution to land-use conflict between solar photovoltaics (PV) and agriculture. Microclimate studies indicate that agrivoltaic systems enhance solar farm cooling, leading to increased panel efficiency, but stakeholders lack efficient design tools to quickly evaluate the consequences of various agrivoltaic designs. Here we present a computational fluid dynamics (CFD)-based machine learning (ML) model which is utilized to develop an early version of an agrivoltaic design tool, where solar cell operating temperature is optimized based on panel height and ground cover type selection. Results indicate that Random Forest Regression (RFR) and Gradient Boosted Trees (GBT) perform better than Support Vector Regression (SVR) and Linear Regression (LR) in this case, with RFR and GBT achieving under 2 °C RMSE compared to SVR and LR over 3 °C. Using dual annealing optimization with the GBT model, findings indicate that panel heights up to 3.2m and ground albedo up to 64% are preferred in warm months to maximize panel cooling.

We show that combining a variety of public datasets and non-linear machine learning models can predict emissions from electric generating units at good accuracy without any proprietary information.