Blockchain based energy transactions for a prosumer community

Data Driven Approach ◽

Energy Assets

Integration of solar micro-generation capabilities in domestic contexts is on the rise, leading to the creation of prosumer communities who generate part of the energy they consume. Prosumer communities require a decentralized, transparent and immutable transaction system in order to extract value from their surplus energy generation and usage flexibility. The aim of this study is to develop frameworks and methods to create such a prosumer transaction system with self enforcing smart contracts to facilitate trading of energy assets such as electricity units, energy flexibility incentives and storage credits. Blockchain is a transparent, distributed ledger for consensus based transaction processing maintained by a network of peer nodes. Hyperledger Fabric is a blockchain platform that offers the added benefits of lower operating cost, faster transaction processing, user authentication based access control and support for self enforcing smart contracts. This thesis investigates the applicability of Hyperledger Fabric to tokenize and transact energy assets in a unified transaction system. Data driven approaches to implement an incentive based energy flexibility system for peak mitigation on the blockchain are also investigated. To this end, the stakeholders for such a transaction management system were identified and their business relationships and interactions were described. Energy assets were encapsulated into blockchain tokens and algorithms were developed and encoded into self enforcing smart contracts based on the stakeholder relationships. A unified transaction framework was proposed that would bring on board all the stakeholders, their trading relationships and the assets being transacted. Tokens and methods in the transaction system were implemented in fungible and non fungible versions and the versions were critically compared in terms of application area, design, algorithmic complexity, performance, advantages and disadvantages. Further, with a focus on energy flexibility applications, a prosumer research dataset was analysed to gain insights into the production and consumption behaviors. Based on these insights, a data driven approach for peak mitigation was proposed and implemented on the Hyperledger Fabric blockchain. The thesis thus addresses different aspects of a blockchain based prosumer transaction system, and shows the feasibility of proposed approaches through implementation and performance testing of proofs of concept.

Social Planning

Encyclopedia of Social Work ◽

10.1093/acrefore/9780199975839.013.367 ◽

2013 ◽

Author(s):

Jon Simon Sager

Keyword(s):

Problem Solving ◽

Community Organizations ◽

Federal Agencies ◽

Data Driven ◽

Social Planning ◽

Future Trends ◽

Objective Data ◽

Target Populations ◽

Social planning emphasizes the application of rational problem-solving techniques and data-driven approaches to identify, determine, and help coordinate services for target populations. Social planning is carried out by a myriad of organizations—from federal agencies to community organizations—attempting to solve problems ranging from child welfare to aging. The advantages and disadvantages of this empirically objective data-driven approach, including different forms, will be discussed along with past, current, and future trends within the field of social work.

Blockchain Based Transaction System with Fungible and Non-Fungible Tokens for a Community-Based Energy Infrastructure

Sensors ◽

10.3390/s21113822 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3822

Author(s):

Nikita Karandikar ◽

Antorweep Chakravorty ◽

Chunming Rong

Keyword(s):

Electric Vehicles ◽

Performance Testing ◽

Detailed Comparison ◽

Community Based ◽

Energy Assets ◽

Usage Flexibility ◽

Lifecycle Management ◽

And Storage ◽

Case Design

Renewable energy microgeneration is rising leading to creation of prosumer communities making it possible to extract value from surplus energy and usage flexibility. Such a peer-to-peer energy trading community requires a decentralized, immutable and access-controlled transaction system for tokenized energy assets. In this study we present a unified blockchain-based system for energy asset transactions among prosumers, electric vehicles, power companies and storage providers. Two versions of the system were implemented on Hyperledger Fabric. Assets encapsulating an identifier or unique information along with value are modelled as non-fungible tokens (NFT), while those representing value only are modelled as fungible tokens (FT). We developed the associated algorithms for token lifecycle management, analyzed their complexities and encoded them in smart contracts for performance testing. The results show that performance of both implementations are comparable for most major operations. Further, we presented a detailed comparison of FT and NFT implementations based on use-case, design, performance, advantages and disadvantages. Our implementation achieved a throughput of 448.3 transactions per second for the slowest operation (transfer) with a reasonably low infrastructure.

Data-driven approaches in FinTech: a survey

Information Discovery and Delivery ◽

10.1108/idd-06-2020-0062 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Xin Tian ◽

Jing Selena He ◽

Meng Han

Keyword(s):

Workforce Development ◽

Portfolio Management ◽

Data Privacy ◽

Design Methodology ◽

Data Driven ◽

Future Directions ◽

Content Type ◽

Analysis Process ◽

Purpose This paper aims to explore the latest study of the emerging data-driven approach in the area of FinTech. This paper attempts to provide comprehensive comparisons, including the advantages and disadvantages of different data-driven algorithms applied to FinTech. This paper also attempts to point out the future directions of data-driven approaches in the FinTech domain. Design/methodology/approach This paper explores and summarizes the latest data-driven approaches and algorithms applied in FinTech to the following categories: risk management, data privacy protection, portfolio management, and sentiment analysis. Findings This paper details out comparison between different existed works in FinTech with traditional data analytics techniques and the latest development. The framework for the analysis process is developed, and insights regarding the implementation, regulation and workforce development are provided in this area. Originality/value To the best of the authors’ knowledge, this paper is first to consider broad aspects of data-driven approaches in the application of FinTech industry to explore the potential, challenges and limitations of this area. This study provides a valuable reference for both the current and future participants.

Colour and Texture Descriptors for Visual Recognition: A Historical Overview

Journal of Imaging ◽

10.3390/jimaging7110245 ◽

2021 ◽

Vol 7 (11) ◽

pp. 245

Author(s):

Francesco Bianconi ◽

Antonio Fernández ◽

Fabrizio Smeraldi ◽

Giulia Pascoletti

Keyword(s):

Deep Learning ◽

Intelligent Systems ◽

Visual Recognition ◽

Early Years ◽

Data Driven ◽

Traditional Methods ◽

Research Attention ◽

Convolutional Networks ◽

Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial (`intelligent’) systems has attracted considerable research attention since the early 70s. Whereas the main approach to the problem was essentially theory-driven (`hand-crafted’) up to not long ago, in recent years the focus has moved towards data-driven solutions (deep learning). In this overview we retrace the key ideas and methods that have accompanied the evolution of colour and texture analysis over the last five decades, from the `early years’ to convolutional networks. Specifically, we review geometric, differential, statistical and rank-based approaches. Advantages and disadvantages of traditional methods vs. deep learning are also critically discussed, including a perspective on which traditional methods have already been subsumed by deep learning or would be feasible to integrate in a data-driven approach.

Data Driven Approach to Forecast Building Occupant Complaints

Construction Research Congress 2020 ◽

10.1061/9780784482865.019 ◽

2020 ◽

Author(s):

Sena Assaf ◽

Mohamad Awada ◽

Issam Srour

Keyword(s):

Data Driven ◽

Proceedings of the 2020 Latin America Unconventional Resources Technology Conference ◽

Analysis and Interpretation of Multi-Source Data at the Hydraulic Fracturing Test Site: A Data-Driven Approach to Improve Well Performance Evaluation in Heterogeneous Formations

10.15530/urtec-2020-1560 ◽

2020 ◽

Author(s):

Shadi Salahshoor

Keyword(s):

Performance Evaluation ◽

Hydraulic Fracturing ◽

Test Site ◽

Data Driven ◽

Well Performance ◽

Source Data ◽

Data Driven Approach ◽

Heterogeneous Formations

Application of Blockchain in KYC Compliance

European Journal of Engineering Research and Science ◽

10.24018/ejers.2020.5.7.1973 ◽

2020 ◽

Vol 5 (7) ◽

pp. 781-784

Author(s):

Shashank R. B. ◽

Chirag Chhabra ◽

Nagaraj G. Cholli

Keyword(s):

Smart Contracts ◽

Current Process ◽

Thomson Reuters

The current process of Know Your Customer (KYC) used by banks is time-consuming, expensive, and redundant in practice. A Thomson Reuters Research states that while banks globally spend around 60 million USD on an average, this number may go up to 500 million USD for some banks [1]. Hence, to improve the efficiency of this process, the use of a blockchain-based mechanism is suggested. The use of smart contracts also provides scope for adding features that cannot be achieved by the current process. The paper majorly discusses the advantages and disadvantages of using blockchain for performing KYC processes.