scholarly journals Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4334
Author(s):  
Yujie Xu ◽  
Vivian Loftness ◽  
Edson Severnini
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Savings ◽  
Detection System ◽  
Effective Means ◽  
The United States ◽  
Data Driven ◽  
Commercial Building ◽  
Carbon Footprints ◽  
Site Energy

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption).

scholarly journals Energy management in sensor networks

2012 ◽  
Vol 370 (1958) ◽  
pp. 52-67 ◽  
Author(s):  
John A. Stankovic ◽  
Tian He
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Management ◽  
Large Scale ◽  
Energy Savings ◽  
Time Synchronization ◽  
Media Access Control ◽  
Media Access ◽  
Energy Aware ◽  
Holistic View

This paper presents a holistic view of energy management in sensor networks. We first discuss hardware designs that support the life cycle of energy, namely: (i) energy harvesting, (ii) energy storage and (iii) energy consumption and control. Then, we discuss individual software designs that manage energy consumption in sensor networks. These energy-aware designs include media access control, routing, localization and time-synchronization. At the end of this paper, we present a case study of the VigilNet system to explain how to integrate various types of energy management techniques to achieve collaborative energy savings in a large-scale deployed military surveillance system.

Advances in Dynamic Virtual Machine Management for Cloud Data Centers

2017 ◽  
pp. 91-114
Author(s):  
Rashmi Rai ◽  
G. Sahoo
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Economic Modeling ◽  
User Preferences ◽  
Virtual Machine Migration ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Computing Services

The ever-rising demand for computing services and the humongous amount of data generated everyday has led to the mushrooming of power craving data centers across the globe. These large-scale data centers consume huge amount of power and emit considerable amount of CO2.There have been significant work towards reducing energy consumption and carbon footprints using several heuristics for dynamic virtual machine consolidation problem. Here we have tried to solve this problem a bit differently by making use of utility functions, which are widely used in economic modeling for representing user preferences. Our approach also uses Meta heuristic genetic algorithm and the fitness is evaluated with the utility function to consolidate virtual machine migration within cloud environment. The initial results as compared with existing state of art shows marginal but significant improvement in energy consumption as well as overall SLA violations.

scholarly journals An Ontology-Based Framework for Building Energy Management with IoT

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 485 ◽  
Author(s):  
Clement Lork ◽  
Vishal Choudhary ◽  
Naveed Ul Hassan ◽  
Wayes Tushar ◽  
Chau Yuen ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Management System ◽  
Energy Savings ◽  
Environmental Parameters ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Commercial Building ◽  
Building Energy Management ◽  
Building Management

In this paper, we develop an ontology-based framework for energy management in buildings. We divide the functional architecture of a building energy management system into three interconnected modules that include building management system (BMS), benchmarking (BMK), and evaluation & control (ENC) modules. The BMS module is responsible for measuring several useful environmental parameters, as well as real-time energy consumption of the building. The BMK module provides the necessary information required to understand the context and cause of building energy efficiency or inefficiency, and also the information which can further differentiate normal and abnormal energy consumption in different scenarios. The ENC module evaluates all the information coming from BMS and BMK modules, the information is contextualized, and finally the cause of energy inefficiency/abnormality and mitigating control actions are determined. Methodology to design appropriate ontology and inference rules for various modules is also discussed. With the help of actual data obtained from three different rooms in a commercial building in Singapore, a case study is developed to demonstrate the application and advantages of the proposed framework. By mitigating the appropriate cause of abnormal inefficiency, we can achieve 5.7%, 11.8% and 8.7% energy savings in Room 1, Room 2, and Room 3 respectively, while creating minimum inconvenience for the users.

scholarly journals Protein Immunoassay Methods for Detection of Biotech Crops: Applications, Limitations, and Practical Considerations

2002 ◽  
Vol 85 (3) ◽  
pp. 780-786 ◽  
Author(s):  
James W Stave
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
The United States ◽  
Enzyme Linked Immunosorbent Assay ◽  
Department Of Agriculture ◽  
Lateral Flow Strip ◽  
Statistical Confidence ◽  
Collaborative Studies ◽  
The Subject ◽  
Biotech Crops

Abstract Immunoassay methods are available for detection and quantitation of proteins expressed by most biotechnology-derived crops in commercial production. The 2 most common test formats are enzyme-linked immunosorbent assay (ELISA) and immunochromatographic (lateral flow) strip tests. Two ELISA methods, one for Roundup Ready soybeans and one for MON810 Cry1Ab corn, were the subject of large international collaborative studies and were demonstrated to quantitatively determine the concentrations of biotech crops in samples of ground grain. Quantitative ELISA methods are also useful for analysis of processed fractions of agricultural commodities such as soybean toasted meal or corn flour. Both strip tests and ELISAs for biotech crops are currently being used on a large scale in the United States to manage the sale and distribution of grain. In these applications, tests are used to determine if the concentration of biotech grain is above or below specified threshold limits. Using existing U.S. Department of Agriculture sampling techniques, the reliability of the threshold determination is expressed in terms of statistical confidence rather than analytical precision. Combining the use of protein immunoassays with Identity Preservation systems provides an effective means of characterizing the raw and processed agricultural inputs to the food production system in a way that allows food producers to comply with labeling laws.

Effects of Container Geometry on Energy Consumption During Hardening in Ice Cream Manufacturing

2010 ◽  
Author(s):  
W. C. Cromer ◽  
Mark J. Miller ◽  
X. J. Xin ◽  
Z. J. Pei ◽  
Karen A. Schmidt
Keyword(s):  
Energy Consumption ◽  
Food Industry ◽  
Energy Savings ◽  
Ice Cream ◽  
The United States ◽  
Dairy Food ◽  
Hardening Process ◽  
Container Shape ◽  
The U.S ◽  
Reducing Energy

Energy consumption by the dairy food industry in the United States constitutes 10% of all energy consumed by the U.S. food industry. Reducing energy consumption in cooling and refrigeration of foods plays an important role in meeting the challenge of the energy crisis. Hardening is an important and energy-intensive step in ice cream manufacturing. This work presents Finite Element Method (FEM) investigation of the ice cream hardening process, aiming to provide insight and guidance for energy savings in ice cream manufacturing. Effects of container shape and dimensions, container layers, and heat transfer boundary conditions on energy consumption for hardening of ice cream were investigated.

scholarly journals Screening performance of abbreviated versions of the UPSIT smell test

2018 ◽  
Author(s):  
Theresita Joseph ◽  
Stephen D. Auger ◽  
Luisa Peress ◽  
Daniel Rack ◽  
Jack Cuzick ◽  
...  
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
Cost Effective ◽  
Data Driven ◽  
Screening Performance ◽  
Smell Test ◽  
Identification Test ◽  
Data Driven Approach ◽  
Smell Identification ◽  
Smell Tests

ABSTRACTBackgroundHyposmia features in several neurodegenerative conditions, including Parkinson’s disease (PD). The University of Pennsylvania Smell Identification Test (UPSIT) is a widely used screening tool for detecting hyposmia, but is time-consuming and expensive when used on a large scale.MethodsWe assessed shorter subsets of UPSIT items for their ability to detect hyposmia in 891 healthy participants from the PREDICT-PD study. Established shorter tests included Versions A and B of both the 4-item Pocket Smell Test (PST) and 12-item Brief Smell Identification Test (BSIT). Using a data-driven approach, we evaluated screening performances of 23,231,378 combinations of 1-7 smell items from the full UPSIT.ResultsPST Versions A and B achieved sensitivity/specificity of 76.8%/64.9% and 86.6%/45.9% respectively, whilst BSIT Versions A and B achieved 83.1%/79.5% and 96.5%/51.8% for detecting hyposmia defined by the longer UPSIT. From the data-driven analysis, two optimised sets of 7 smells surpassed the screening performance of the 12 item BSITs (with validation sensitivity/specificities of 88.2%/85.4% and 100%/53.5%). A set of 4 smells (Menthol, Clove, Gingerbread and Orange) had higher sensitivity for hyposmia than PST-A, -B and even BSIT-A (with validation sensitivity 91.2%). The same 4 smells also featured amongst those most commonly misidentified by 44 individuals with PD compared to 891 PREDICT-PD controls and a screening test using these 4 smells would have identified all hyposmic patients with PD.ConclusionUsing abbreviated smell tests could provide a cost-effective means of screening for hyposmia in large cohorts, allowing more targeted administration of the UPSIT or similar smell tests.

Development and Testing of an Automated Building Commissioning Analysis Tool (ABCAT)

2010 ◽  
Author(s):  
John D. Bynum ◽  
David E. Claridge ◽  
Jonathan M. Curtin
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Weather Conditions ◽  
The United States ◽  
Commercial Buildings ◽  
Test Case ◽  
Analysis Tool ◽  
Component Level ◽  
Building Commissioning ◽  
Occupant Comfort

Experience has shown that buildings on average may consume 20% more energy than required for occupant comfort which by one estimate leads to $18 billion wasted annually on energy costs in commercial buildings in the United States. Experience and large scale studies of the benefits of commissioning have shown the effectiveness of these services in improving the energy efficiency of commercial buildings. While commissioning services do help reduce energy consumption and improve performance of buildings, the benefits of the commissioning tend to degrade over time. In order to prolong the benefits of commissioning, a prototype fault detection and diagnostic (FDD) tool intended to aid in reducing excess energy consumption known as an Automated Building Commissioning Analysis Tool (ABCAT) has been developed. ABCAT is a first principles based whole building level top down FDD tool which does not require the level of expertise and money often associated with more detailed component level methods. The model based ABCAT tool uses the ASHRAE Simplified Energy Analysis Procedure (SEAP) which requires a smaller number of inputs than more sophisticated simulation methods such as EnergyPlus or DOE-2. ABCAT utilizes a calibrated mathematical model, white box method, to predict energy consumption for given weather conditions. A detailed description of the methodology is presented along with test application results from more than 20 building years worth of retrospective applications and greater than five building years worth of live test case applications. In this testing, the ABCAT tool was used to successfully identify 24 significant energy consumption deviations in five retrospective applications and five significant energy consumption deviations in four live applications.

Energy Efficiency in K-12 Schools: A Case Study in Florida

2020 ◽  
Author(s):  
Ganesh Doiphode ◽  
Hamidreza Najafi ◽  
Mariana Migliori Favaretto
Keyword(s):  
Energy Efficiency ◽  
Public Schools ◽  
Energy Consumption ◽  
Energy Savings ◽  
The United States ◽  
Payback Period ◽  
Background Information ◽  
Subtropical Climate ◽  
Institute Of Technology ◽  
K 12

Abstract Buildings are one of the largest energy consumers in the United States. K-12 schools are responsible for nearly 8% of energy consumption by commercial buildings which is equivalent to 1.44% of total annual energy consumption in the country. Understanding the baseline energy consumption of the schools as well as identifying effective energy efficiency measures (EEMs) that result in significant energy savings without compromising occupant’s comfort in a given climate condition are essential factors in moving towards a sustainable future. In a collaboration between Florida Institute of Technology and Brevard Public Schools, three schools are identified for a test study in Melbourne, FL, representing the humid subtropical climate. Energy audit is conducted for these schools and monthly utility bill data as well as background information, end-user’s data and their associated operating schedules are obtained. A detailed analysis is performed on the utility bill data and energy consumption by each end-user is estimated. Several EEMs are considered and evaluated to achieve an improved energy efficiency for the schools. The implementation cost of each EEM and the associated simple payback period is also determined. A study is also conducted to explore possibility of using solar power to cover 50% of energy requirements of each school and the cost and payback period of the project are evaluated. The results of this paper provide insights regarding prioritizing energy efficiency projects in K-12 schools in humid subtropical climates and particularly the state of Florida and help with decision making regarding investment in on-site power generation using solar energy.

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