Establishing a Commercial Buildings Energy Data Framework for India: A Comprehensive Look at Data Collection Approaches, Use Cases and Institutions
4
Citation
1
Reference
10
Related Paper
Citation Trend
Abstract:
Enhancing energy efficiency of the commercial building stock is an important aspect of any national energy policy. Understanding how buildings use energy is critical to formulating any new policy that may impact energy use, underscoring the importance of credible data. Data enables informed decision making and good quality data is essential for policy makers to prioritize energy saving strategies and track implementation. Given the uniqueness of the buildings sector and challenges to collecting relevant energy data, this study characterizes various elements involved in pertinent data collection and management, with the specific focus on well-defined data requirements, appropriate methodologies and processes, feasible data collection mechanisms, and approaches to institutionalizing the collection process. This report starts with a comprehensive review of available examples of energy data collection frameworks for buildings across different countries. The review covers the U.S. experience in the commercial buildings sector, the European experience in the buildings sector and other data collection initiatives in Singapore and China to capture the more systematic efforts in Asia in the commercial sector. To provide context, the review includes a summary and status of disparate efforts in India to collect and use commercial building energy data. Using this review as a key input, the study developed a data collection framework for India with specific consideration to relevant use cases. Continuing with the framework for data collection, this study outlines the key performance indicators applicable to the use cases and their collection feasibility, as well as immediate priorities of the participating stakeholders. It also discusses potential considerations for data collection and the possible approaches for survey design. With the specific purpose of laying out the possible ways to structure and organize data collection institutionally, the study collates existing mechanisms to analyze building energy performance in India and opportunities for standardizing data collection. This report describes the existing capacities and resources for establishing an institutional framework for data collection, the legislation and mandates that support such activity, and identifies roles and responsibilities of the relevant ministries and organizations. Finally, the study presents conclusions and identifies two major data collection strategies within the existing legal framework.In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth `V' of big data is increasingly being recognized. In this tutorial, we highlight the substantial challenges that the first three `V's, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner. With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the "data to speak for itself" in order to discover the semantics of the data. This tutorial presents recent results that are relevant to big data quality management, focusing on the two major dimensions of (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency, and identifies a range of open problems for the community.
Linked Data
Cite
Citations (237)
An experiment was conducted comparing electromc versus pencil-and-paper data collection methods. The consistent Ending was that the electromc data collection method displayed more stability across levels of the methodological variables. No significant differences were found in the responses received for the data collection methods or interactions. The findings for these two primary topics of study combined with the absence of significant differences across data collection methods for measures of three additional variables, involvement with the product, attitude toward survey research and questionnaire completion time, lead to the conclusion that electronic and pencil-and-paper data collection approaches provide data that is equivalent and interchangeable in many if not all ways. Keywords Electromc data collection, data collection methods, response effects, decision processes, protocol analysis, survey research.
Pencil (optics)
Cite
Citations (24)
An Analysis of the Mixed Collection Modes for two Business Surveys Conducted by the US Census Bureau
In the past decade, offering multiple modes of data collection has become increasingly popular. However, the benefits of offering multiple modes should not come at the cost of data quality. Using historic data from two federal business surveys, we investigate data quality as a function of mode of data collection using various quality measures, including the unit response rate (the unweighted proportion of responding reporting units) and the quantity response rate (the weighted proportion of an estimate obtained from reported data).
Mode (computer interface)
Cite
Citations (10)
The process of data collection has a very big influence on the overall quality of the CPI, especially its timelines and accuracy. The paper discusses the importance of the design of price collection forms and the selection of data collectors with pro
Cite
Citations (1)
Data governance
Unstructured data
Cite
Citations (19)
This paper examined the roles and problems of data collection for student evaluation. They following areas are discussed. Meaning of data collection, roles of data collection for student evaluation, types of data collection viz-a-viz their roles in an evaluation process. Problem of data collection as it affects evaluation during teaching and learning process were also examined.
Cite
Citations (2)
Bioclimatic data support several researches that aim to identify the influence of climatic factors on biodiversity on the planet. In order to make these studies possible, the quality of the data that supports the analyzes must be guaranteed from the beginning of the data life cycle so that the results and models generated reflect the real scenario of the investigated phenomena. However, the collection of climate and biodiversity data presents significant challenges. This work performs a survey of the main quality problems identified in the bioclimatic data collection process. The methodological procedure consisted in identifying the problems, assigning the data quality dimension affected, and suggestions for possible solutions to the problems. The results of this survey showed that ambiguous methodological procedures during the data gathering and human interference are important factors for data quality impairment and the information obtained from these data. Thus, the correction of the data should focus on the collection processes and procedures, not the raw data itself.
Survey data collection
Cite
Citations (4)
This paper considers the place of data science in remote sensing and points out a review of relevant issues. Data science (DSci) as a discipline has been appropriately traced to the concern for data quality mostly as an input to complex decision-making situations based on examination through different approaches of very large volumes of data. The emerging massive data volumes in many fields of science, including remote sensing and other scientific and technological fields have fueled a review of the tenet of data quality assessment and analyses of the types of massive data that DSci relies on. In this paper we succinctly review these data quality concerns as a basis for our projects where a variety of massive data is considered.
Data type
Cite
Citations (0)
Data quality management systems are thoroughly researched topics and have resulted in many tools and techniques developed by both academia and industry. However, the advent of Big Data might pose some serious questions pertaining to the applicability of existing data quality concepts. There is a debate concerning the importance of data quality for Big Data; one school of thought argues that high data quality methods are essential for deriving higher level analytics while another school of thought argues that data quality level will not be so important as the volume of Big Data would be used to produce patterns and some amount of dirty data will not mask the analytic results which might be derived. This paper aims to investigate various components and activities forming part of data quality management such as dimensions, metrics, data quality rules, data profiling and data cleansing. The result list existing challenges and future research areas associated with Big Data for data quality management.
Data cleansing
Profiling (computer programming)
Cite
Citations (57)
<span>Recently Big Data has become one of the important new factors in the business field. This needs to have strategies to manage large volumes of structured, unstructured and semi-structured data. It’s challenging to analyze such large scale of data to extract data meaning and handling uncertain outcomes. Almost all big data sets are dirty, i.e. the set may contain inaccuracies, missing data, miscoding and other issues that influence the strength of big data analytics. One of the biggest challenges in big data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics and unpredictable conclusions. Data cleaning is an essential part of managing and analyzing data. In this survey paper, data quality troubles which may occur in big data processing to understand clearly why an organization requires data cleaning are examined, followed by data quality criteria (dimensions used to indicate data quality). Then, cleaning tools available in market are summarized. Also challenges faced in cleaning big data due to nature of data are discussed. Machine learning algorithms can be used to analyze data and make predictions and finally clean data automatically.</span>
Data Analysis
Unstructured data
Data set
Cite
Citations (47)