Energy Information Systems: From the Basement to the Boardroom

A significant buildings energy reduction opportunity exists in the office sector, given that this market segment typically is an early adopter of new technology. There is a rising trend towards smart and connected offices through the internet of things (IoT) that provides new opportunities for operational efficiency and environmental sustainability practices. Leading commercial real estate companies have begun to shift from individual building automation systems (BAS) to partially integrated and automated systems such as energy information systems (EIS). In both the United States and India, organizations are seeking operational excellence, enhanced tenant relationships, and topline growth. Hence it is imperative to engage the executives with decision-making power, by tapping into their interest in sustainability, corporate social responsibility, and innovation. This expansion of interest can enable data-driven decisions, strong energy investments, and deeper energy benefits, and would drive innovation in this field. However, none of this would be possible without robust, consistent building energy information to provide visibility across all the levels of decision making, i.e. from the basement where the facilities staff take operational action to the boardroom where the executives make investment decisions. Price, security, and ease of use remain barriers to the adoption and pervasive use of promising EIS technologies in commercial office buildings. We believe that these barriers can be addressed through the development of ready, simplified, consistent, commercially available, low-cost EIS-in-a-box packages, that have a pre-defined set of hardware components and software features and functionality that are pertinent to a particular building sector. These simplified, sector-specific EIS packages can help to obviate the need for customization, and enhance ease of use, thereby enabling scale-up, in order to facilitate building energy savings. The EIS-in-a-box are adaptable in both U.S. and Indian office buildings, and potentially beyond these two countries

Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version

An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

Understanding evolutionary processes during past Quaternary climatic cycles: Can it be applied to the future?

Climate change affected ecological community make-up during the Quaternary which was probably both the cause of, and was caused by, evolutionary processes such as species evolution, adaptation and extinction of species and populations