Industrial Energy Pattern Clustering for Production-Aware Analysis
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Solution Overview
Problem
Current methods for energy management in industrial plants lack integration and fail to effectively detect and analyze energy inefficiencies across the entire facility, limiting their ability to optimize energy use.
Innovation Solution
A computer-implemented method using IoT sensors and clustering algorithms to analyze energy consumption patterns, identify inefficiencies, and correlate them with production data, enabling the detection of anomalies and updates to energy management strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data mining algorithms are used to determine temporal behaviors of utility consumption, then usage patterns for individual devices can be obtained, but integrated energy management of the whole industrial plant cannot be performed
Solution Approach 1:
The system segments energy management into hierarchical levels (individual devices, production lines, entire plant) while maintaining integration capability. Clustering algorithms group energy consumption data from multiple sensors to identify patterns at different granularities, enabling both device-level analysis and plant-wide integrated management simultaneously
Solution Approach 2:
The system merges data from multiple sources including energy meters, production data systems, and environmental sensors into a unified analysis framework. By combining these diverse data streams and applying clustering algorithms, the system achieves integrated energy management that spans from individual devices to the entire industrial plant
2Measurement precision
If energy consumption data is collected and analyzed without correlation to production data, then energy patterns can be identified, but the cause of energy inefficiencies cannot be properly evaluated
Solution Approach 1:
The system establishes a feedback loop where energy consumption data is continuously correlated with production data. Clustering algorithms identify energy patterns and compare them against production variations, providing feedback that enables differentiation between energy changes caused by production variations versus actual inefficiencies
Solution Approach 2:
The system creates a universal correlation framework that works across different production processes and energy consumption scenarios. By establishing generalizable relationships between production data and energy consumption through clustering, the system can evaluate energy inefficiencies across diverse industrial contexts while preserving specific contextual information
Data Source
AI summary
A method for evaluating energy consumption in an industrial plant, includes capturing sensor data from at least one level of the industrial plant, from the sensor data, digitally obtaining energy consumption curves, an energy consumption curve representing, along a certain time period, discrete values of energy consumption corresponding to time intervals Δt. The method also includes applying a clustering algorithm for digitally computing K energy consumption patterns, wherein each pattern represents a set of energy consumption curves grouped according to a similarity metric and includes discrete values of energy consumption corresponding to time intervals Δt; capturing data of production achieved during the time period at at least one level; calculating the aggregated sum of the discrete values, thus obtaining the aggregated energy consumption for each curve; and digitally establishing a relationship between aggregated energy consumption for each energy consumption curve during the time period and captured data of production.