Predictive Analytics for Power Transformers and Energy Consumption with Machine Learning
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This chapter discusses the implications of predictive analytics for consumer privacy and surveys the existing law that could reach predictive analytics in ecommerce. Part II summarizes the prevailing theoretical accounts of privacy. Part III introduces predictive analytics and illustrates its potential uses in ecommerce. Part IV examines how using predictive analytics in ecommerce affects consumer privacy. Part V examines how existing state and federal law could reach merchants’ use of predictive analytics in ecommerce. Finally, Part VI concludes by summarizing the uncertain application of existing law to predictive analytics.
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The usage of predictive analytics is lifting its head in the HR area. The business benefits of using predictive analytics in sales are self-evident, but in HR the value is more difficult to prove due to non-monetary and non-standardized measurements. As predictive analytics in general is not yet widely used in Finland, the companies are cautious in taking the first steps towards this capability. The purpose of this study is to explore and identify the possible business benefits of implementing predictive analytics into the HR area. The basic building blocks needed for predictive analytics are also covered, as well as the main challenges companies identify, in order to understand what could be hindering the analytics evolution in the HR area. Whereas descriptive analytics concentrates on creating reports and summaries of the past, predictive analytics aims to understand the past but also complements it by understanding the correlations of events, by estimating the future and by predicting probabilities for the whole employee lifecycle; recruiting success, employee management risks and employee retention. The new capabilities delivered though the predictive analytics are meant to help today’s HR professionals in making better decisions related to HR activities, accelerating the processes and by eliminating the error of the sole human interpretation. As to the results of the study, the main benefits perceived were very company specific. However, all the companies saw the greatest value in using predictive analytics in the HR areas they identified to have the biggest business challenges in, or which were otherwise near their core business. Additionally, the most value for predictive analytics was identified specifically in four HR functions; employee acquisition, employee retention, employee engagement and employee well-being. Predictive analytics supports the HR activities, through which the benefits can be gained; increased employee engagement and satisfaction and enhanced performance resulting in increased company performance, customer satisfaction, sales and profitability increase and to cost reductions. Recommendation for each company is to start with quick predictive analytics trials in the areas they perceive as valuable. The companies perceive their main challenges to rise from the lack of people who would understand both predictive analytics and HR business. Also the general level of the analytics maturity and data harmonization and integration were seen as challenges. Some interviewed companies wanted to have the basic building blocks in place, such as improved data governance processes, data integrations and optimal data quality, before taking the next steps. However, this study encourages the companies to start with targeted actions and to tie the measurements to financial figures with predictive analytics, in order to reach the identified business opportunities.
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Predictive analytics is used in variety of industries such as banking, healthcare and insurance sector.
However, most of the predictive models are built using solely historical data. This makes much
predictive model failed to work well in fields where the factors affecting future events are qualitative
(e.g. news). In this project, a predictive model based on artificial neural network which take
quantitative and qualitative data as input is proposed to increase the prediction accuracy. The
predictive model will be trained and tested on two datasets which is short term power load dataset
and stock price dataset.
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Predictive analytics includes many statistical and other empirical methods that create various data predictions as well as different methods for assessing predictive power. Predictive analytics not only helps in creating practically useful models but also plays an important role in building new theory for further study and research. Today, the use of available data to extract inferences and predictions by using predictive analytics has grown in the industry from being a small department in large companies to being an active component in most mid to large sized organizations. This paper addresses to reduce a particularly large gap of, the nearabsence of empirical or factual predictive analytics in the mainstream research going on in this field by analyzing the issues faced in predictive modelling by the empirical determination of data with its experimental facts for latency pattern. Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning.
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Abstract In this paper, I critically examine ethical issues introduced by predictive analytics. I argue firms can have a market incentive to construct deceptively inflated true‐positive outcomes: individuals are over‐categorized as requiring a penalizing treatment and the treatment leads to mistakenly thinking this label was correct. I show that differences in power between firms developing and using predictive analytics compared to subjects can lead to firms reaping the benefits of predatory predictions while subjects can bear the brunt of the costs. While profitable, the use of predatory predictions can deceive stakeholders by inflating the measurement of accuracy, diminish the individuality of subjects, and exert arbitrary power. I then argue that firms have a responsibility to distinguish between the treatment effect and predictive power of the predictive analytics program, better internalize the costs of categorizing someone as needing a penalizing treatment, and justify the predictions of subjects and general use of predictive analytics. Subjecting individuals to predatory predictions only for a firms' efficiency and benefit is unethical and an arbitrary exertion of power. Firms developing and deploying a predictive analytics program can benefit from constructing predatory predictions while the cost is borne by the less powerful subjects of the program.
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Data analytics is proving to be very useful for achieving productivity gains in manufacturing. Predictive analytics (using advanced machine learning) is particularly valuable in manufacturing, as it leads to production improvement with respect to the cost, quantity, quality and sustainability of manufactured products by anticipating changes to the manufacturing system states. Many small and medium manufacturers do not have the infrastructure, technical capability or financial means to take advantage of predictive analytics. A domain-specific language and framework for performing predictive analytics for manufacturing and production frameworks can counter this deficiency. In this paper, we survey some of the applications of predictive analytics in manufacturing and we discuss the challenges that need to be addressed. Then, we propose a core set of abstractions and a domain-specific framework for applying predictive analytics on manufacturing applications. Such a framework will allow manufacturers to take advantage of predictive analytics to improve their production.
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The past few years have seen an explosion in the business use of analytics. Corporations around the world are using analytical tools to gain a better understanding of their customer's needs and wants. Predictive analytics has become an increasingly hot topic in analytics landscape as more companies realize that predictive analytics enables them to reduce risks, make intelligent decisions, and create differentiated customer experiences. As a result, predictive analytics deployments are gaining momentum. Yet, the adoption rate is slow, and organizations are only beginning to scratch the surface in regards to the potential applications of this technology. Implemented properly, the business benefits can be substantial. However, there are strategic pitfalls to consider. The key objective of this article is to propose a conceptual model for successful implementation of predictive analytics in organizations. This article also explores the changing dimensions of analytics, highlights the importance of predictive analytics, identifies determinants of implementation success, and covers some of the potential benefits of this technology. Furthermore, this study reviews key attributes of a successful predictive analytics platform and illustrates how to overcome some of the strategic pitfalls of incorporating this technology in business. Finally, this study highlights successful implementation of analytics solutions in manufacturing and service industry.
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