Energy Consumption Prediction Using Operational Condition Extraction
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Solution Overview
Problem
Existing energy consumption prediction techniques rely solely on date information, leading to inaccurate predictions as they fail to account for relevant operational conditions, resulting in suboptimal prediction accuracy.
Innovation Solution
An energy consumption prediction device that stores past energy consumption data correlated with date and time, along with incidental operational information, allowing users to designate extraction conditions and perform clustering and statistical processing to generate more accurate prediction data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only date information is used for energy consumption prediction, then the prediction method is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent segments the prediction approach by dividing the data extraction process into multiple dimensions: date information, incidental information (operation conditions, weather, holidays), and user-defined extraction conditions. This segmentation allows each dimension to be handled separately while combining them for comprehensive prediction, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent transitions from one-dimensional date-based prediction to multi-dimensional prediction by incorporating incidental information (operation conditions, weather data, holiday information) and user-defined extraction conditions. This dimensional expansion enables more accurate predictions without proportionally increasing system complexity.
2Ease of operation
If incidental information is extracted using only date designation, then the data extraction process is simple, but relevant operational conditions cannot be properly identified
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple types of extraction conditions (operation conditions, weather conditions, holiday conditions) that can be selected and configured before data extraction. This allows the system to be prepared for comprehensive information extraction while maintaining user-friendly operation through pre-configured options.
Solution Approach 2:
The extraction unit is designed with multi-functionality to handle various types of extraction conditions universally. It can extract data based on date, operation conditions, weather, holidays, or any combination thereof, making the system adaptable to different prediction needs without requiring separate extraction processes for each condition type.
3Measurement precision
If comprehensive incidental information is collected, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent implements dynamics by allowing flexible configuration of extraction conditions based on user needs. Users can dynamically select which types of incidental information to extract (operation conditions, weather, holidays) and adjust extraction parameters without modifying the underlying system structure. This dynamic adaptability enables comprehensive data collection when needed while maintaining simplicity when fewer data types are required.
Data Source
AI summary
An energy consumption prediction device includes: a storage unit configured to store past energy consumption result data of a target facility in correlation with information for identifying a date and time at which the data has been acquired and incidental information which is information related to an operation situation of the target facility when the data has been acquired; an extraction unit configured to extract data in which the incidental information matches an extraction condition designated by a user from the energy consumption result data stored in the storage unit and to generate prediction data which is used to predict an amount of consumed energy; and a prediction data generating unit configured to generate energy consumption prediction data in accordance with an instruction from the user based on the prediction data.