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Special Issue of the IBM Journal of Research and Development
Large amounts of data are transforming industries, and this has a particularly important and significant effect in the energy domain. Energy stands at the nexus of areas relating to the environment, economic development, and national security. This crucial industry is also positioned at the tipping point of multiple disruptive trends.
As noted by our guest editors IBM Fellow Chandu Visweswariah, director of the IBM Smarter Energy Research Institute, and Brad Gammons, Jr., general manager of IBM’s Global Energy and Utilities Industry, we also face an unprecedented amount of uncertainty in planning, managing, and orchestrating energy systems. For example, consider weather uncertainty, uncertainty in supply and demand, intermittency due to renewable energy adoption, an uncertain regulatory environment, and uncertainty in energy prices.
At the same time, we are drowning in data from SCADA (Supervisory Control And Data Acquisition) instrumentation, PMUs (Phasor Measurement Units), and smart meters—along with exogenous data like weather characteristics, energy usage, customer needs, vegetation characteristics, soil data, and so on. When curated and mined with the appropriate analytics, this data will help the industry to transition to clean energy, increase customer awareness and participation, and improve the resilience and efficiency of our energy systems.
This issue is devoted to the innovative application of cutting-edge analytics in the energy domain. A collection of 11 papers represent state-of-the-art tools that are delivering significant value in utilities around the world. This body of work demonstrates the importance of such topics to IBM as well the importance of analytics in the energy sector. IBM Research, in all its global labs, has embraced deep domain knowledge and industry eminence in the energy space, leading to innovations that are not just useful in the “back office,” but that will prove essential to the day-to-day operations of energy companies.
Clifford A. Pickover
IBM Journal of Research and Development