Systems and methods for processing different data types

Abstract

Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.

What is claimed: 
     
         1 . A computer-implemented method for data processing, comprising:
 receiving data from a plurality of different data sources in canonical form;   partitioning the data into two or more different data types, wherein the two or more different data types comprise (1) a first type of data comprising structured or slow-changing data, and (2) a second type of data comprising dynamically streaming data;   storing the first type of data in a relational database and separately storing the second type of data in a non-relational database; and   applying a plurality of steps for processing the first and second types of data based on their types, wherein the plurality of steps comprise:
 processing the first type of data for performing aggregate calculations and performance denormalization on first type of data; 
 processing the second type of data for normalizing the second type of data by addressing outliers in the second type of data; and 
 applying a machine learning algorithm to the first and second types of data to identify usage or consumption trends or abnormal events associated with one or more energy meters. 
   
     
     
         2 . The method of  claim 1 , wherein addressing outliers comprises addressing aberrational values of energy usage. 
     
     
         3 . The method of  claim 1 , wherein processing the first type of data is performed directly on the first type of data in the relational database, and processing the second type of data is performed directly on the second type of data in the non-relational database. 
     
     
         4 . The method of  claim 1 , wherein applying the plurality of steps further comprises generating an alert based at least in part on the usage or consumption trends or abnormal events. 
     
     
         5 . The method of  claim 4 , wherein the usage comprises an unexpected change in energy usage. 
     
     
         6 . The method of  claim 4 , wherein the usage comprises energy loss associated with the one or more energy meters. 
     
     
         7 . The method of  claim 4 , wherein the alert is generated in real-time or near real-time. 
     
     
         8 . The method of  claim 1 , wherein the first and second types of data is processed on a temporal basis. 
     
     
         9 . The method of  claim 8 , further comprising generating an energy usage report on a daily, monthly, or yearly basis based at least in part on the processing. 
     
     
         10 . The method of  claim 9 , further comprising using the energy usage report to identify trends associated with one or more Key Performance Indicators (KPIs) comprising energy cost, energy consumption, energy cost per square area, and energy consumption per square area. 
     
     
         11 . The method of  claim 10 , wherein the energy usage report further comprises energy efficiency recommendations and load shedding opportunities. 
     
     
         12 . The method of  claim 1 , wherein applying the machine learning algorithm to the first and second types of data comprises feature extraction for identifying the usage or consumption trends or the abnormal events, wherein the feature extraction comprises (a) extracting a plurality of features associated with the usage or consumption trends or the abnormal events and (b) identifying one or more data sources that exhibit the usage or consumption trends or the abnormal events. 
     
     
         13 . The method of  claim 12 , wherein the usage or consumption trends or the abnormal events comprise malfunction and/or theft. 
     
     
         14 . The method of  claim 13 , wherein applying the machine learning algorithm to the first and second types of data comprises feature classification comprising providing the one or more data sources identified as exhibiting the malfunction and/or theft to a classification model configured to apply different weights to the plurality of features based on signature types in order to classify each of the one or more data sources as having a signature of malfunction or a signature of theft. 
     
     
         15 . The method of  claim 14 , wherein applying the machine learning algorithm to the first and second types of data further comprises feature ranking comprising generating a priority order for resolving the malfunction and/or theft of the one or more data sources based on an impact of the malfunction and/or theft on business operations of an entity 
     
     
         16 . The method of  claim 1 , wherein the received data comprises a plurality of canonical objects having different type definitions and description. 
     
     
         17 . The method of  claim 16 , wherein the plurality of canonical objects are associated with an organization, facility, service, billing, usage-point, meter-reading, energy conservation measure, external benchmark, and/or region. 
     
     
         18 . The method of  claim 1 , wherein the dynamically streaming data is received in streams, and wherein a data stream is persisted until the processing of the data stream is complete, at which time the processed data stream is discarded. 
     
     
         19 . The method of  claim 1 , wherein the two or more different data types are associated with conceptual models of attributes and processed related to different entities or domains. 
     
     
         20 . The method of  claim 1 , wherein the canonical form is based on industry standards or specifications.