Insights for OS X Used for Probabilistic Renewable Energy Forecasting
Berlin, Germany - KnowledgeMiner Software today is pleased to announce the remarkable application of their critically acclaimed collection of self-organizing, automated forecasting technologies implemented in Insights for OS X to a set of high-priority probabilistic energy forecasting problems. Based on a 12-week rolling real-world forecasting scenario in four categories - electric load, electricity price, wind and solar power forecasting - for up to 10 sites, Insights' self-learning forecasting skills ended up in top five positions in all categories of the Global Energy Forecasting Competition (GEFCom2014) organized by the IEEE Power & Energy Society and the University of North Carolina at Charlotte.
"With only four teams out of more than 500 from over 40 countries successfully finished all four challenging GEFCom2014 forecasting tracks, this underlines the very high predictive power and productivity of Insights for Mac OS X right out-of-the-box. We are happy and proud to provide this proven high quality to our customers to allow them to easily, fast, and reliably turn data into knowledge to solve complex and urgent problems in their field," Frank Lemke of KnowledgeMiner Software commented.
Insights for OS X is a professional, yet convenient tool for building predictive models from data of complex systems autonomously. Taking observational data that describes a problem, system, or process, the software constructs a working mathematical model. Compatible with data stored in a variety of popular formats (e.g., Microsoft Excel), its AI-powered, self-organizing modeling algorithms allow users to easily extract new and useful knowledge to support decision-making. All models can be exported to Excel, AppleScript, or Matlab for further deployment.
- Original, high-dimensional, self-learning, inductive knowledge mining with ease
- Automatically develops models and model ensembles from data, and it generates the equation that describes the data as a mathematical interpretation of how the model works
- Integrates all complex tasks, such as variables selection from up to 2000 inputs, knowledge extraction, model development and validation, into one process and hides it from user
- Live Prediction Validation technology
- Model export to Microsoft Excel, ready-to-use AppleScript code, or TEXT to be used in Matlab
- Powered by 64-bit, parallel, cross-platform (OS X, Windows), self-organizing modeling engine for multi-core CPUs and projects integration
Complex systems demand that we abandon the classical, hard approach of creating analytical models using theoretical systems analysis, since we often have incomplete knowledge of the processes involved, which forces us to make vague guesses about the internal workings at various stages of the analysis. Environmental, social, and socio-economic systems are but three examples. This approach is time consuming, inflexible, and leads to results that are questionable, ambiguous, and qualitative in nature. These challenges, as well as the nascent Internet of Things, require a paradigm shift in model building toward inductive modeling and forecasting technologies based on self-organization.
Insights for OS X is applied to health and life sciences related problems, sales prediction, financial and resource planning, engineering problems, climate change modeling and forecasting, and many others, and it is currently used by NASA, MIT, Columbia University, Merck, Mobil, Pfizer, and many other corporations, universities, research institutes, and individuals worldwide.
- English, Spanish, and German
- OS X 10.7 or later
- Any Mac with 64-bit CPU, 8 GB RAM recommended
- Minimum screen resolution of 1280 x 768 pixel
- For Excel support, Excel versions 2011 or 2008
Pricing and Availability:
Insights for OS X is available as a Free, Advanced or Pro version exclusively from the KnowledgeMiner Software website. Academic versions can be purchased by contacting KnowledgeMiner Software directly. Review copies are available on request.
Located in Berlin, Germany, KnowledgeMiner Software was founded in 1993 by Frank Lemke. The company is active in research, development, consulting, and application of self-organizing modeling and knowledge discovery technologies. It developed and implemented a number of original technologies for validation of inductively built data mining models. KMS has been doing consulting in model development and prediction of toxicological and eco-toxicological hazards and risks of chemical compounds from experimental data for regulatory purposes within REACH and participated in three international research projects funded by the European Commission related to QSAR. Other fields of activity have been climate change related modeling and prediction problems, sales and demand predictions, macro- and micro-economic modeling problems like national economy and balance sheet prediction, energy consumption analysis and prediction, medical diagnosis, and wastewater reuse problems.
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