18395871. Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program simplified abstract (Mitsubishi Electric Corporation)
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 18395871 titled 'Power Consumption Estimation Device, Power Consumption Estimation Method, and Non-transitory Computer Readable Storage Medium Storing Power Consumption Estimation Program
Simplified Explanation
The abstract describes a patent application for a CPU that performs multiple regression analysis to calculate the power consumption of different facilities. Here is a simplified explanation of the abstract:
- The CPU uses regression models to calculate the contribution of each target facility to the total power consumption.
- It then determines the power consumption of non-monitored facilities by subtracting the tentative power consumption of the target facility from the total power consumption.
- The CPU classifies the power consumption data of non-monitored facilities into clusters.
- It uses a second regression model to determine the contribution of each target facility and calculates their power consumption based on this determination.
- Potential Applications
This technology could be applied in energy management systems for optimizing power consumption in various facilities such as buildings, factories, or data centers.
- Problems Solved
This technology helps in accurately estimating the power consumption of different facilities, even those that are not directly monitored, leading to better energy management and cost savings.
- Benefits
The benefits of this technology include improved energy efficiency, cost savings, and better resource allocation based on accurate power consumption data.
- Potential Commercial Applications
Potential commercial applications of this technology include energy management software, smart building systems, and industrial automation solutions.
- Possible Prior Art
One possible prior art for this technology could be existing energy management systems that use regression analysis for power consumption estimation.
- Unanswered Questions
- How does the CPU handle outliers in the power consumption data during regression analysis?
The abstract does not provide information on how the CPU deals with outliers in the power consumption data, which could affect the accuracy of the calculations.
- What is the computational complexity of the regression analysis performed by the CPU?
The abstract does not mention the computational complexity of the regression analysis, which could impact the real-time performance of the system.
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.
