Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method

Rapid growth of smart metering data in smart grids provides great opportunities for the retailer to design customized price schemes and demand side management (DSM) programs for different customer groups. This paper proposes a hybrid data-driven method of clustering customers' daily load profiles and optimizing different electricity retail plan recommendations for electricity retailers. By combing the user-side information with the risk-aware decision-making framework, specifically using conditional value-at-risk (CVaR) modeling method, the retailer could guarantee its accumulated revenue without doing any harm to the customers' benefit, while guiding their energy consumption behavior instead. Through large-scale experiments, it is observed that a slight increase in the customers' possible payment would be compensated by their big gain in more demand response opportunities. The retailers' profit could also be increased by roughly 49%-51% and 33%-38% with or without enabling demand response programs.acceptedVersionPeer reviewe

Video advertisement mining for predicting revenue using random forest

Shaken by the threat of financial crisis in 2008, industries began to work on the topic of predictive analytics to efficiently control inventory levels and minimize revenue risks. In this third-generation age of web-connected data, organizations emphasized the importance of data science and leveraged the data mining techniques for gaining a competitive edge. Consider the features of Web 3.0, where semantic-oriented interaction between humans and computers can offer a tailored service or product to meet consumers\u27 needs by means of learning their preferences. In this study, we concentrate on the area of marketing science to demonstrate the correlation between TV commercial advertisements and sales achievement. Through different data mining and machine-learning methods, this research will come up with one concrete and complete predictive framework to clarify the effects of word of mouth by using open data sources from YouTube. The uniqueness of this predictive model is that we adopt the sentiment analysis as one of our predictors. This research offers a preliminary study on unstructured marketing data for further business use

The impact of social media marketing on working adults’ purchase intention via e-commerce after Covid-19 pandemic

Technological advancement has been vulnerable in complimenting how consumer conduct their buying behavior. Social media, has fast been becoming a popular platform for dealers to reach customers when promoting and introducing products o gain more business opportunities and profits. Social Media continues to evolve in facilitating business and service growth by incorporating improved features to ease online interactions. This research aims to determine the impact of social media marketing on the factors influencing online purchasing intention via e-commerce among working adults in Malaysia after the Covid-19 pandemic. The pandemic has made online purchasing an excellent choice for conducting business transactions, mainly for safety and fear of getting into close contact with persons infected by the deadly virus. Hence, it is necessary to explore the insights on consumer buying intention from the perspective of scientific research in the business discipline that has become the objective of this study. The findings have successfully proven that the independent variables, i.e., the perceived Customer Trust (CT), perceived Product Usefulness (PU), perceived Service Quality (SQ), and perceived Social Media Marketing (SMM), do have significantly influenced the consumers purchasing intention after Covid-19 pandemic. The impact of social media marketing on working adults’ purchase intention via e-commerce after Covid-19 has resulted in a positive future outlook, especially towards business entities. The influence of social media marketing itself towards working adults can help those involved in advertisement and content-creating industries to continuously conduct research with the possibility of taking up their creative skills to a higher level. (Abstract by author

Quality of experience in affective pervasive environments

The confluence of miniaturised powerful devices, widespread communication networks and mass remote storage has caused a fundamental shift in the user interaction design paradigm. The distinction between system and user in pervasive environments is evolving into an increasingly integrated loop of interaction, raising a number of opportunities to provide enhanced and personalised experiences. We propose a platform, based on a smart architecture, to address the identified opportunities in pervasive computing. Smart systems aim at acting upon an environment for improving quality of experience: a subjective measure that has been defined as an emotional reaction to products or services. The inclusion of an emotional dimension allows us to measure individual user responses and deliver personalised services with the potential to influence experiences positively. The platform, Cloud2Bubble, leverages pervasive systems to aggregate user and environment data with the goal of addressing personal preferences and supra-functional requirements. This, combined with its societal implications, results in a set of design principles as a concrete fruition of design contractualism. In particular, this thesis describes: - a review of intelligent ubiquitous environments and relevant technologies, including a definition of user experience as a dynamic affective construct; - a specification of main components for personal data aggregation and service personalisation, without compromising privacy, security or usability; - the implementation of a software platform and a methodological procedure for its instantiation; - an evaluation of the developed platform and its benefits for urban mobility and public transport information systems; - a set of design principles for the design of ubiquitous systems, with an impact on individual experience and collective awareness. Cloud2Bubble contributes towards the development of affective intelligent ubiquitous systems with the potential to enhance user experience in pervasive environments. In addition, the platform aims at minimising the risk of user digital exposure while supporting collective action.Open Acces

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse