A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030

As part of its Paris Agreement commitment, China pledged to peak carbon dioxide (CO2) emissions around 2030, striving to peak earlier, and to increase the non-fossil share of primary energy to 20% by 2030. Yet by the end of 2017, China emitted 28% of the world's energy-related CO2 emissions, 76% of which were from coal use. How China can reinvent its energy economy cost-effectively while still achieving its commitments was the focus of a three-year joint research project completed in September 2016. Overall, this analysis found that if China follows a pathway in which it aggressively adopts all cost-effective energy efficiency and CO2 emission reduction technologies while also aggressively moving away from fossil fuels to renewable and other non-fossil resources, it is possible to not only meet its Paris Agreement Nationally Determined Contribution (NDC) commitments, but also to reduce its 2050 CO2 emissions to a level that is 42% below the country's 2010 CO2 emissions. While numerous barriers exist that will need to be addressed through effective policies and programs in order to realize these potential energy use and emissions reductions, there are also significant local environmental (e.g., air quality), national and global environmental (e.g., mitigation of climate change), human health, and other unquantified benefits that will be realized if this pathway is pursued in China

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Finding the different patterns in buildings data using bag of words representation with clustering

The understanding of the buildings operation has become a challenging task due to the large amount of data recorded in energy efficient buildings. Still, today the experts use visual tools for analyzing the data. In order to make the task realistic, a method has been proposed in this paper to automatically detect the different patterns in buildings. The K Means clustering is used to automatically identify the ON (operational) cycles of the chiller. In the next step the ON cycles are transformed to symbolic representation by using Symbolic Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag of words representation for hierarchical clustering. Moreover, the proposed technique is applied to real life data of adsorption chiller. Additionally, the results from the proposed method and dynamic time warping (DTW) approach are also discussed and compared

China and East Asian Energy - Prospects and Issues Volume II Part I

This collection of papers in two volumes is the second in a series on China and East Asian Energy, a major project which is an initiative of the East Asia Forum in conjunction with the China Economy and Business Program in the Crawford School of Economics and Government at the Australian National University (ANU). The first volume was published in April 2007. The research program is directed at understanding the factors influencing Chinas energy markets. It also involves high-level training and capacity building to foster long-term links between policy thinkers in China and Australia. It provides for regular dialogue with participants from the energy and policy sectors in the major markets in East Asia and Australia. The backbone of the dialogue is an annual conference, the location of which has thus far alternated between Beijing and Canberra. The objective is to advance a research agenda that informs and influences the energy policy discussion in China, Australia and the region. This special edition of the Asia Pacific Economic Papers brings together papers presented at the second conference in the series. Due to their number and length, papers from that second conference are published across two volumes of the Asia Pacific Economic Papers. This volume includes the first half of the papers, while the next volume includes the second half. The third conference in the project is scheduled for July 2008.China, Energy, East Asia

The Coming Boom in Computer Loads

Computers and other electronic equipment now consume as much electricity as electric steel furnaces, and their growth shows no signs of slowing. Utilities are active participants in the computer revolution. Northeast Utilities, for example, reports that 20% of electricity use in a typical new office building in its service area goes to computers. Given the expected growth in computers and computer loads, this technology deserves greater attention from utility planners and other energy analysts. It is shown that the commercial sector has been the largest contributor to kilowatt-hour (kwh) sales growth and that new uses within the commercial sector have accounted for the biggest portion of this growth. Confirming this conclusion are a 4-year Department of Energy-funded study of the Park Plaza Building office tower and a 1985 study of 181 office buildings by Northwest Utilities. A prospective study suggests that computers could account for as much as 150 billion kwh by the early 1990s