Smart Thermostat Machine Learning for Multi-Source Energy Forecasting
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Solution Overview
Problem
Existing smart thermostats lack the ability to utilize additional data beyond user interaction for learning and predicting energy consumption, and they do not integrate data from multiple thermostats to optimize energy management.
Innovation Solution
A system and method that synchronizes smart thermostat data with weather data, trains machine learning models, and uses typical annual weather data to predict energy consumption changes, enabling energy consumption forecasting and optimization across multiple thermostats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If smart thermostats use only user interaction data for learning, then the system remains simple, but energy consumption prediction accuracy is insufficient
Solution Approach 1:
The patent combines multiple data sources including user interaction data, weather data, and data from multiple thermostats into a unified machine learning model. This merging of diverse data streams enables more accurate energy consumption predictions while managing complexity through systematic data integration architecture.
Solution Approach 2:
The system introduces a central server or cloud platform as an intermediary that collects, processes, and analyzes data from multiple thermostats and weather services. This intermediary handles the complex data integration tasks, allowing individual thermostats to remain relatively simple while achieving enhanced prediction accuracy through aggregated intelligence.
2Productivity
If smart thermostats do not integrate data from multiple thermostats, then the system architecture remains simple, but energy management optimization is limited
Solution Approach 1:
The patent merges data from multiple thermostat installations to create a broader dataset for training machine learning models. By combining experiences from numerous thermostats across different buildings and climates, the system achieves superior energy management optimization that transcends individual thermostat capabilities.
Solution Approach 2:
The system creates a universal platform that serves multiple functions: collecting data from various thermostats, processing weather information, training machine learning models, and providing optimization recommendations. This multi-functional architecture enables the system to handle diverse energy management scenarios while maintaining a unified approach.
3Measurement precision
If smart thermostats lack weather data integration, then the system remains simple, but energy consumption forecasting capability is insufficient
Solution Approach 1:
The system uses a central platform as an intermediary to fetch, synchronize, and integrate weather data from external services with thermostat operational data. This intermediary handles the complexity of data synchronization, ensuring that weather information is properly aligned with thermostat readings for accurate forecasting.
Solution Approach 2:
The system performs preliminary actions by collecting and preprocessing weather data in advance before it is needed for energy consumption forecasting. This proactive data preparation ensures that relevant weather information is already available and properly formatted when required for predictions, improving forecasting accuracy.
Data Source
AI summary
A method includes receiving thermostat data from a thermostat over an initial time period. Weather data is received from a weather service for the initial time period, the thermostat data is synchronized with the weather data with respect to time. The synchronized thermostat and weather data are separated into a plurality of subperiods of the initial time period, each of the subperiods covering substantially equal time. At least one machine learning model is trained using the synchronized thermostat and weather data that is separated into the plurality of subperiods. Typical annual weather data for a location of the building is received and the trained machine learning model is used with the received typical annual weather data, the received thermostat temperature setpoint data, and at least one change to a parameter that impacts energy consumption by the building to determine an expected change in energy consumption by the building.