Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability

Building modeling, specifically heating, ventilation, and air conditioning (HVAC) load and equivalent energy storage calculations, represent a key focus for decarbonization of buildings and smart grid controls. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to widespread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing smart homes managed by the Tennessee Valley Authority is used. The HMLM employs combined k-means clustering and multiple linear regression (MLR) models to predict minutely HVAC power with satisfactory nRMSE error of less than 10% across an entire year test set. An approach is provided to characterize HVAC systems through the newly proposed hybrid model as a generalized storage (GES) device suitable for DER control and event types in accordance with the Communication Technology Association (CTA) 2045 standard and Energy Star metrics such as “energy take”, currently developed by industry, to unify household appliance controls

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads with Application to Energy Management of Smart and NZE Homes

Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In addition to being used in the proposed HVAC separation method, the LSTM models are employed also for accurate day-ahead HVAC and solar photovoltaic (PV) energy forecasts. To test and validate the proposed method, the SHINES dataset, a publicly available dataset spanning three years at 15-minute time resolution from a large-scale DOE experimental project, is used. Two computational case studies are constructed with a test HEMS to investigate the power regulating capability of smart home virtual operation as dispatchable loads or generators. Prediction results obtained with the proposed method show hourly and daily CV(RMSE) of 29.4% and 11.1%, respectively. These results are well within the bounds of error established by academia and the ASHRAE building model and calibration standards

Changing the narrative: The role of frontline worker attitudes and beliefs in shaping dementia friendly services in England

Applying the main principles of the social model of disability as a guide, this article argues that the attitudes and beliefs of staff and volunteers employed in frontline service delivery can play an important role in the achievement of dementia-friendly communities, particularly through influencing the types of services offered. This position is supported by findings from an evaluation of an awareness-raising intervention run by Age UK, aimed at organisations which provide services for people living with dementia in England. The article contributes to an understanding of the cultural climate within frontline service delivery, which is often neglected in favour of discussions around meeting more immediate care and support needs. More specifically, the article reflects on whether there is a need for an additional conceptualisation within the discourse around dementia-friendly communities which ensures inclusion of the cultural environment

Demand Response of HVACs in Large Residential Communities Based on Experimental Developments

Heating, ventilation, and air-conditioning (HVAC) systems contribute the largest electricity usage for a residential community. Modeling of the HVAC systems facilitate the study of demand response (DR) at both the residential and the power system level. In this paper, the equivalent thermal model of a reference house was proposed. Parameters for the reference house were determined based on the systematic study of experimental data obtained from fully instrumented field demonstrators. The aggregated HVAC load was modeled based on the reference house while considering a realistic distribution of HVAC parameters derived from data that was provided by one of the largest smart grid field demonstrators in rural America. A sequential DR as part of a Virtual Power Plant (VPP) control was proposed to reduce both ramping rate and peak power at the aggregated level, while maintaining human comfort according to ASHRAE standard

The Effect of High Efficiency Building Technologies and PV Generation on the Energy Profiles for Typical US Residences

The penetrations of high efficiency technologies and photovoltaic (PV) generation are increasing in the residential sector. Technologies such as improved insulation and efficient HVAC systems significantly affect the energy profile of a house. This effect varies due to climate characteristics, i.e. temperature, solar radiation, relative humidity, and wind speeds. The effect of other technologies, such as efficient water heaters, lighting, or kitchen appliances, is mainly governed by human behavior, which may be represented by a schedule. This paper studies the performance of both climate-influenced and scheduled household devices among different levels of efficiency through combined computational and experimental methods. Three houses were constructed by the Tennessee Valley Authority and were outfitted with robots that mimicked the occupation of a family. The houses represented three categories of residences, namely, typical builder, retrofit, and near net-zero-energy. With the energy and weather data collected from 2009 to 2014, a total of four house energy models were developed to account for equipment changes throughout the years. The studies performed using these models considered the behavior of the HVAC systems, PV system, and water heaters as well as climate effects

Virtual Power Plant Control for Large Residential Communities Using HVAC Systems for Energy Storage

Heating, ventilation, and air-conditioning (HVAC) systems use the most electricity of any household appliance in residential communities. HVAC system modeling facilitates the study of demand response (DR) at both the residential and power system levels. In this article, the equivalent thermal model of a reference house is proposed. Parameters for the reference house were determined based on the systematic study of experimental data obtained from fully instrumented field demonstrators. Energy storage capacity of HVAC systems is calculated and an equivalent state-of-charge is defined. The uniformity between HVAC systems and battery energy storage system is demonstrated by DR control. The aggregated HVAC load model is based on the reference house and considers a realistic distribution of HVAC parameters derived from one of the largest smart grid field demonstrators in rural America. A sequential DR scheme as part of a virtual power plant control is proposed to reduce both ramping rate and peak power at the aggregated level, while maintaining human comfort according to ASHRAE standards

Index divisibility in dynamical sequences and cyclic orbits modulo p

Let φ(x) = x d + c be an integral polynomial of degree at least 2, and consider the sequence (φ n (0))∞n=0, which is the orbit of 0 under iteration by φ. Let Dd,c denote the set of positive integers n for which n | φ n (0). We give a characterization of Dd,c in terms of a directed graph and describe a number of its properties, including its cardinality and the primes contained therein. In particular, we study the question of which primes p have the property that the orbit of 0 is a single p-cycle modulo p. We show that the set of such primes is finite when d is even, and conjecture that it is infinite when d is odd

Total Tumor Load Assessed by One-Step Nucleic Acid Amplification Assay as an Intraoperative Predictor for Non-Sentinel Lymph Node Metastasis in Breast Cancer

BACKGROUND: This study aimed to determine the relationship between CK19 mRNA copy number in sentinel lymph nodes (SLN) assessed by one-step nucleic acid amplification (OSNA) technique, and non-sentinel lymph nodes (NSLN) metastization in invasive breast cancer. A model using total tumor load (TTL) obtained by OSNA technique was also constructed to evaluate its predictability. METHODS: We conducted an observational retrospective study including 598 patients with clinically T1-T3 and node negative invasive breast cancer. Of the 88 patients with positive SLN, 58 patients fulfill the inclusion criteria. RESULTS: In the analyzed group 25.86% had at least one positive NSLN in axillary lymph node dissection. Univariate analysis showed that tumor size, TTL and number of SLN macrometastases were predictive factors for NSLN metastases. In multivariate analysis just the TTL was predictive for positive NSLN (OR 2.67; 95% CI 1.06-6.70; P = 0.036). The ROC curve for the model using TTL alone was obtained and an AUC of 0.805 (95% CI 0.69-0.92) was achieved. For TTL >1.9 × 105 copies/μL we got 73.3% sensitivity, 74.4% specificity and 88.9% negative predictive value to predict NSLN metastases. CONCLUSION: When using OSNA technique to evaluate SLN, NSLN metastases can be predicted intraoperatively. This prediction tool could help in decision for axillary lymph node dissection.info:eu-repo/semantics/publishedVersio

Training and development experiences of nursing associate trainees based in primary care across England: a qualitative study.

Background: The nursing associate role was first deployed in England in 2019 to fill a perceived skills gap in the nursing workforce between healthcare assistants and registered nurses and to offer an alternative route into registered nursing. Initially, trainee nursing associates were predominantly based in hospital settings; however, more recently, there has been an increase in trainees based in primary care settings. Early research has focussed on experiences of the role across a range of settings, particularly secondary care; therefore, little is known about the experiences and unique support needs of trainees based in primary care. Aim: To explore the experiences and career development opportunities for trainee nursing associates based in primary care. Methods: This study used a qualitative exploratory design. Semi-structured interviews were undertaken with 11 trainee nursing associates based in primary care from across England. Data were collected between October and November 2021, transcribed and analysed thematically. Findings: Four key themes relating to primary care trainee experiences of training and development were identified. Firstly, nursing associate training provided a ‘valuable opportunity for career progression’. Trainees were frustrated by the ‘emphasis on secondary care’ in both academic content and placement portfolio requirements. They also experienced ‘inconsistency in support’ from their managers and assessors and noted a number of ‘constraints to their learning opportunities’, including the opportunity to progress to become registered nurses. Conclusion: This study raises important issues for trainee nursing associates, which may influence the recruitment and retention of the nursing associate workforce in primary care. Educators should consider adjustments to how the curriculum is delivered, including primary care skills and relevant assessments. Employers need to recognise the resource requirements for the programme, in relation to time and support, to avoid undue stress for trainees. Protected learning time should enable trainees to meet the required proficiencies

Dirac electrons in graphene-based quantum wires and quantum dots

In this paper we analyse the electronic properties of Dirac electrons in finite-size ribbons and in circular and hexagonal quantum dots made of graphene.Comment: Contribution for J. Phys.: Cond. Mat. special issue on graphene physic