Energy Feedback System Using Sensor and Bill Data Modeling
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Solution Overview
Problem
Consumers lack effective tools to understand and reduce their domestic energy consumption, as existing systems fail to provide accurate and actionable feedback on energy usage by appliance categories, leading to missed opportunities for energy savings.
Innovation Solution
A system comprising sensors to monitor energy consumption, a processor to generate a model of energy usage by combining bill data, sensor data, and regional statistics, and a user interface to provide detailed feedback, allowing users to adjust their behavior for energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a system provides detailed breakdown of energy usage by category, then user understanding and ability to save energy is improved, but the complexity of data processing and model generation increases
Solution Approach 1:
The system segments total energy consumption into distinct categories (heating, lighting, appliances, cooking, hot water) using a processor that analyzes sensor data and bill information. This segmentation provides users with detailed breakdowns of where energy is consumed, enabling targeted behavior changes in specific high-usage areas while managing complexity through automated categorization algorithms.
Solution Approach 2:
The system introduces an intermediary processing layer that includes a processor, database, and communication interface. This intermediary receives raw sensor data and bill information, processes it through modeling algorithms, and presents simplified category breakdowns to users. The intermediary manages the complexity by handling data integration, validation, and presentation formatting, shielding users from the underlying system complexity.
2Measurement precision
If the system uses multiple data sources (bill data, sensor data, regional statistics) to generate accurate models, then measurement precision is improved, but the quantity of data and processing requirements increases
Solution Approach 1:
The system merges three distinct data sources: sensor-collected consumption data, bill information (total cost and kWh), and regional average statistics. The processor integrates these diverse data types into a unified energy usage model that categorizes consumption by appliance and function. This merging improves measurement precision by cross-validating data across sources while the automated integration process manages the complexity of handling multiple data streams.
Solution Approach 2:
The system employs a universal data processing framework that handles multiple data types (sensor readings, bill formats, statistical data) through a single integrated processor and database structure. This multi-functional approach allows the same system architecture to process various data sources and generate comprehensive energy models, improving accuracy without proportionally increasing processing complexity through standardized data handling procedures.
3Loss of information
If the system provides real-time monitoring and feedback, then user awareness and energy saving behavior is improved, but the energy consumption of the monitoring system itself increases
Solution Approach 1:
The monitoring system operates autonomously using sensors that automatically collect consumption data, a processor that continuously generates energy usage models, and a communication interface that provides real-time feedback to users without manual intervention. This self-service operation maintains continuous monitoring capability while minimizing the energy overhead of the monitoring system itself, as the processors leverage existing bill data and regional statistics rather than requiring additional active measurement infrastructure.
Data Source
AI summary
A system to provide feedback for energy saving to a user of a property comprising a plurality of appliances, the system comprising: at least one sensor monitoring energy consumption of one or more appliances within the property; a user interface to provide feedback to the user; and a processor configured to receive input data from an energy bill for the property covering a predetermined period; receive input regional average statistics regarding energy consumption for a set of predetermined categories of energy usage; generate a model of energy usage within a plurality of categories over the predetermined period by combining the input data, energy consumption data from the at least one sensor and generic statistics regarding energy consumption and output, via the user interface, feedback to the user based on the generated model.