Mitsubishi electric corporation (20240135468). Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program simplified abstract
Contents
- 1 Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Original Abstract Submitted
Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program
Organization Name
mitsubishi electric corporation
Inventor(s)
Shinichiro Otani of TOKYO (JP)
Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135468 titled 'Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program
Simplified Explanation
The patent application describes a method for analyzing power consumption data of different facilities using regression models to determine their contribution to the total power consumption. Here is a simplified explanation of the abstract:
- A CPU uses regression models to calculate the contribution of each target facility to total power consumption.
- It then estimates the power consumption of each facility based on their contribution.
- The CPU classifies power consumption data of non-monitored facilities into clusters.
- Another regression analysis is performed to determine the contribution of each target facility.
- The power consumption of each target facility is calculated based on the determined contribution.
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- Potential Applications
This technology can be applied in various industries such as energy management, facility optimization, and resource allocation.
- Problems Solved
This technology helps in identifying the power consumption patterns of different facilities, optimizing energy usage, and improving overall efficiency.
- Benefits
- Efficient resource allocation - Cost savings through optimized energy consumption - Enhanced facility management and maintenance
- Potential Commercial Applications
"Optimizing Power Consumption Analysis Technology" can be used in smart buildings, industrial facilities, and data centers for energy management and cost optimization.
- Possible Prior Art
Prior art in this field includes traditional regression analysis methods for data analysis and energy management systems used in commercial buildings.
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- Unanswered Questions
- How does this technology handle real-time data analysis?
The patent application does not specify the real-time capabilities of the system. It would be important to understand the latency and processing speed of the CPU in handling real-time power consumption data.
- What are the limitations of the regression models used in this technology?
The patent application does not discuss the potential limitations of the regression models employed. It would be beneficial to know the accuracy and reliability of the models in predicting power consumption.
Original Abstract Submitted
a cpu performs multiple regression analysis using a first regression model to calculate a tentative degree of contribution of each target facility to the total power consumption. the cpu calculates tentative power consumption of the target facility using the tentative degree of contribution of the target facility. the cpu calculates power consumption of a non-monitored facility by subtracting the total value of the tentative power consumption of the target facility from the total power consumption. the cpu classifies time-series data of the power consumption of the non-monitored facility into a plurality of clusters. the cpu performs multiple regression analysis using a second regression model to determine the degree of contribution of each of the target facilities. the cpu determines the power consumption of the target facility using the determined degree of contribution.
