Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Risk Management in the Arctic Offshore: Wicked Problems Require New Paradigms

Recent project-management literature and high-profile disasters—the financial crisis, the BP Deepwater Horizon oil spill, and the Fukushima nuclear accident—illustrate the flaws of traditional risk models for complex projects. This research examines how various groups with interests in the Arctic offshore define risks. The findings link the wicked problem framework and the emerging paradigm of Project Management of the Second Order (PM-2). Wicked problems are problems that are unstructured, complex, irregular, interactive, adaptive, and novel. The authors synthesize literature on the topic to offer strategies for navigating wicked problems, provide new variables to deconstruct traditional risk models, and integrate objective and subjective schools of risk analysis

Emission-aware Energy Storage Scheduling for a Greener Grid

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the ACM International Conference on Future Energy Systems (e-Energy 20) June 2020, Australi

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Anthropology and business: reflections on the business applications of cultural anthropology.

Today's business have international and intercultural dimensions. The complexity of market, organizational climate and culture and the management of human resources demand interdisciplinary and intercultural approach which are available in anthropological researches and methods. The consumer world has its own developments, diversifications and psycho-cultural fermentation. These changes pose new challenges for the designers and suppliers of products, services, systems and processes. Many changes in the economic and social spheres are beyond the range of conventional number-led, straight-line anaysis and planning. Rapid discontinuous changes defy all straight-line forecasting and conventional plannings. Qualitative and open-ended researches, scenario planning and brainstorming sessions, which skilled anthropologists are able to provide, are necessary to face such challenges.

Space-Time Forecasting Using Soft Geostatistics: A Case Study in Forecasting Municipal Water Demand for Phoenix, AZ

Managing environmental and social systems in the face of uncertainty requires the best possible forecasts of future conditions. We use space-time variability in historical data and projections of future population density to improve forecasting of residential water demand in the City of Phoenix, Arizona. Our future water estimates are derived using the first and second order statistical moments between a dependent variable, water use, and an independent variable, population density. The independent variable is projected at future points, and remains uncertain. We use adjusted statistical moments that cover projection errors in the independent variable, and propose a methodology to generate information-rich future estimates. These updated estimates are processed in Bayesian Maximum Entropy (BME), which produces maps of estimated water use to the year 2030. Integrating the uncertain estimates into the space-time forecasting process improves forecasting accuracy up to 43.9% over other space-time mapping methods that do not assimilate the uncertain estimates. Further validation studies reveal that BME is more accurate than co-kriging that integrates the error-free independent variable, but shows similar accuracy to kriging with measurement error that processes the uncertain estimates. Our proposed forecasting method benefits from the uncertain estimates of the future, provides up-to-date forecasts of water use, and can be adapted to other socioeconomic and environmental applications.

Leveraging risk management in the sales and operations planning process

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 71-72).(cont.) Lastly, we visited SemiCo, a leading global supplier of high performance semiconductor products, to gain first-hand insight into the S&OP process of a large multinational company and complete a brief case study about how risk management is currently being utilized within this company's S&OP process. Finally, we synthesized these four sources of information in order to develop a common framework and recommendations that companies can use for understanding the best practices for incorporating risk management into the S&OP process.The objective of this thesis project is to analyze how companies can utilize risk management techniques in their sales and operations planning process (S&OP). S&OP is a strategy used to integrate planning and processes across functional groups within a company, such as sales, operations, and finance. A large body of academic and industry literature already exits, proving that S&OP can integrate people, processes, and technology leading to improved operational performance for a business. However, little research has been done in the area of applying risk management techniques to the S&OP process. When companies use S&OP in order to align their demand, supply, capacity, and production, based on various factors such as history, pricing, promotions, competition, and technology, they rarely factor in uncertainty and risk into the S&OP process. Furthermore, for those companies that do implement risk management in the S&OP process, there is no consensus in the business community about how to do this accurately and effectively. Our basic approach to understanding risk management and its place in the S&OP process will be four-fold. First, we conducted a literature review in order to gain basic S&OP process understanding and current risk management strategies. Next, we conducted thirteen hour-long phone interviews with practitioners and thought leaders in the field of sales and operations planning in order to gain insight into how companies currently discuss, assess, and act upon uncertainty within the S&OP process. Third, we conducted an online survey of various companies and consultants working in the field of S&OP to see how they currently discuss and incorporate uncertainty into their S&OP work.by Yanika Daniels and Timothy Kenny.M.Eng.in Logistic