A method is provided for analyzing energy or environmental factors relative to energy. One or more processors of a data management system receives a plurality of signals relating to a plurality of streams of data. The signals directed to at least one of: energy physical assets, energy related environment factors, energy availability, energy usage and energy source. A data transformer is used to transform the data to item and attribute characterizations. The data transformer uses a processor to organize the first, second and third streams of data according to their associated items and attributes. One or more analytic engines are used to receive the items with their attributes from the data management system and provide multi-dimensional analysis relative to a scale of study for at least one business intelligence, where multi-dimensional analysis is defined as analysis that compares, calculates, correlates, or operates on items and/or attributes from at least two distinct dimensions. The one or more processor of the data management system are used to provide multi-dimensional analysis of the data for at least one business intelligence that is defined as contributing insight into the impacts of physical assets on environmental resources or cultural factors, or the impacts of environmental resources or cultural factors on physical assets in the context of a question related to quantity, quality or efficiency of one of more business activities. The one or more analytic engines provide one or more outputs of processed and correlated data that is sent to an analysis qualifier to determine a quality of analysis. The data analysis evaluator determines a quality score for information generated by the analytic engine.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for analyzing energy or environmental factors relative to energy, comprising: receiving at one or more processors of a data management system a plurality of signals relating to a plurality of streams of data, the signals directed to at least one of: energy physical assets, energy related environment factors, energy availability, energy usage and energy source; using a data transformer to transform the data to item and attribute characterizations, the data transformer using a processor to organize the first, second and third streams of data according to their associated items and attributes; using one or more analytic engines to receive the items with their attributes from the data management system and provide multi-dimensional analysis relative to a scale of study for at least one business intelligence, where multi-dimensional analysis is defined as analysis that compares, calculates, correlates, or operates on items and/or attributes from at least two distinct dimensions, using the processor of the data management system to provide multi-dimensional analysis of the data for at least one business intelligence that is defined as contributing insight into the impacts of physical assets on environmental resources or cultural factors, or the impacts of environmental resources or cultural factors on physical assets in the context of a question related to quantity, quality or efficiency of one of more business activities; using the one or more analytic engines to provide one or more outputs of processed and correlated data that is sent to an analysis qualifier to determine a quality of analysis; using data analysis evaluator to determine a quality score for information generated by the analytic engine. 2. The method of claim 1, further comprising: using the one or more analytic engines to perform at least one of correlations, calculations and comparisons of the one or more impacts of physical assets on energy resources, or the impacts of energy resources on physical assets. 3. The method of claim 2, further comprising: using the one or more the analytic engines to determine energy use compared to energy requirements or needs. 4. The method of claim 1, wherein the data management system includes a data aggregator coupled to the at least one of an analytic engine, and a data analyzer. 5. The method of claim 4, wherein the data management system further includes a data aggregator model. 6. The method of claim 1, further comprising: using a data classifier to classify at least a portion of the plurality of stream of data into a data classification selected from at least one of: an energy asset, an environment factor, an energy value, an energy availability; and an energy source. 7. The method of claim 2, further comprising: sending the data classification to a data scale processor to provide a scale of the data relating to energy. 8. The method of claim 1, wherein the physical assets relate to at least one of, capital and operation allocations for at least one of: energy sources, energy availability, energy usage, energy related infrastructure, energy collection and energy facilities. 9. The method of claim 1, further comprising: using a data validator to validate the corrected of the at least one stream of data, with the at least one stream of data selected from at least one of the energy type of format, energy type and energy value, energy thresholds. 10. The method of claim 4, further comprising: using an energy data resolver to accept or reject sensor input relative to one or more of, energy physical assets, energy related environment factors, energy availability, energy usage and energy source. 11. The method of claim 1, further comprising: using a data resolver to transform the one or more signals. 12. The method of claim 1, further comprising: using an analysis and report preparation system to prepare reports relative to one or more of: energy physical assets, energy related environment factors, energy availability, energy usage and energy source. 13. The method of claim 1, further comprising: using a priorities builder to choose a relative importance of the values to a variety of objectives. 14. The method of claim 1, further comprising: using a feedback Interpreter relative to one or more of, energy physical assets, energy related environment factors, energy availability, energy usage and energy source. 15. The method of claim 1, further comprising: applying adaptive intelligence to perform at least one of: recognizing patterns, measuring predicted verses actual outcomes, performing an accuracy of information step, all related to one or more of, energy physical assets, energy related environment factors, energy availability, energy usage and energy source. 16. A method for analyzing business intelligence applied to at least one of physical assets and environment factors, comprising: using a computing system that includes one or more processors of a data management system for at least one stream of data selected from at least one of physical assets and environment factors; using the one or more processors to provide multi-dimensional analysis of the at least one stream of data for at least one of, environmental resources, the impacts of environmental resources and the impacts on physical assets; and using the one or more processors to correlate data from the multi-dimensional analysis. 17. The method of claim 16, wherein the environmental resources are directed to energy management. 18. The method of claim 16, wherein the environmental resources are directed to at least one of energy use or energy consumption. 19. The method of claim 16, further comprising: determining a plurality of inputs from a plurality of sensors; and applying business intelligence analysis to the inputs to identify energy usage conditions associated with non-technical loss. 20. The method of claim 16, further comprising: applying business intelligence analysis to generate a plurality of representations for a plurality of energy usage conditions, the plurality of representations generated based on the inputs for the plurality of sensors; and producing an output that identifies non-technical loss. 21. The method of claim 20, wherein the business intelligence analysis classifies at least a third portion of the plurality of sensor outputs within an allowable dimensional proximity from a first portion of sensor outputs as corresponding to non-technical loss and that classifies at least a fourth portion of sensor outputs within an allowable dimensional proximity from the second portion of sensor outputs as corresponding to normal energy usage. 22. The method of claim 17, further comprising: receiving new sensor inputs from the plurality of sensors that are associated with an energy usage condition; generating a new representation for a particular energy usage condition based on the new sensor outputs; and using a data classifier to classify the new representation. 23. The method of claim 20, further comprising: applying business intelligence analysis to the new representation to modify the data from a data classifier. 24. The method of claim 22, further comprising: identifying the new sensor outputs as corresponding to non-technical loss; reporting the non-technical loss to an energy provider associated with the particular energy usage condition. 25. The method of claim 22, further comprising: acquiring, from the energy provider, at least one of a confirmation or a non-confirmation regarding an energy usage condition. 26. The method of claim 22, further comprising: modifying data from the data classifier based on the at least one of the confirmation or the non-confirmation. 27. The method of claim 16, wherein business intelligence analysis is associated with at least one of a support vector machine, a boosted decision tree, a classification tree, a regression tree, a bagging tree, a random forest, a neural network, or a rotational forest. 28. The method of claim 16, further comprising: identifying a plurality of measurement devices that have likelihoods of being associated with the non-technical loss; and ranking the plurality of measurement devices based on the likelihoods of being associated with the non-technical loss. 29. The method of claim 16, further comprising: determining that at least some of the plurality of measurement devices meet specified ranking threshold criteria; and identifying the at least some of the plurality of measurement devices as candidates for investigation. 30. The method of claim 17, wherein one or more sensor inputs from the sensors provide sensor input selected from at least one of: an account attribute category, an anomalous load sensor input category, a calculated status input category, a current analysis sensor input category, a missing data sensor input category, a disconnected sensor input category, a measurement device event sensor input category, a monthly measurement device anomalous load sensor input category, a monthly measurement device consumption on inactive sensor input category, an outage sensor input category, a stolen measurement device sensor input category, an unusual production sensor input category, a work order sensor input category, or a zero reads sensor input category. 31. The method of claim 17, further comprising: acquiring a set of formulas for the set of sensor outputs, each formula in the set of formulas corresponding to a respective input from the plurality of sensors; and calculating input for at least a plurality of inputs based on the set of formulas. 32. The method of claim 17, wherein at least a portion of the inputs are in response to data acquired from a plurality of measurement devices associated with a plurality of energy usage conditions. 33. The method of claim 17, wherein a first input is generated in response to modification of a second input from the plurality of sensors. 34. The method of claim 1, further comprising: receiving from an energy provider at least one input not included in the plurality of inputs relating to energy usage conditions to identify non-technical loss. 35. The method of claim 2, wherein the business intelligence includes an unsupervised process, and wherein the unsupervised process utilizes unclassified data for identifying non-technical loss. 36. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: selecting a set of signal inputs relating to one or more of: the signals directed to at least one of: energy physical assets, energy related environment factors, energy availability, energy usage and energy source; determining input values for at least a portion of the plurality of sensors; and applying multi-dimensional analysis to the input values to identify energy usage conditions associated with non-technical loss. 37. A method for qualifying and analyzing business intelligence applied to physical assets and environment factors, comprising: using one or more processors of a data management system a plurality of stream of source data, to receive first and second streams of source data, the first stream of data being client provided energy management physical assets and the second stream being of data being environmental resources directed to energy management or usage; using the one or more processors to organize the first and second streams of data according to their associated items and attributes; using the one or more processors to provide multi-dimensional analysis of the at least one stream of environmental data for one or more of: the signals directed to at least one: energy physical assets, energy related environment factors, energy availability, energy usage, using business intelligent to analyze the impacts of the environmental data on physical assets for at least one business intelligence that is defined as contributing insight into the impacts of physical assets on energy management or usage, the one or more processors taking a progression of complexity of analysis and findings as inputs of a first order to produce an output and is tags by scale and attribute, the first order of analysis being available as inputs for second order analysis that become outputs which are summarized; using the one or more processors to correlate data from the multi-dimensional analysis using user-inputted priorities; and using the one or more processors of a data analysis evaluator of the analytic engine to communicate with the client to determine relevance of the analyzed data to question business intelligence, the one or more processors using business intelligence as improved knowledge about a business problem or a study criteria to make an informed decision.