Smart Meter Machine Learning Model for Individual Consumption Forecasting
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Solution Overview
Problem
Traditional commodity consumption forecasting methods struggle to provide accurate forecasts at the individual meter level, as they fail to account for unique usage habits, occupancy fluctuations, and appliance preferences, and are not adaptable to evolving consumption behaviors and external factors.
Innovation Solution
Implementing machine learning techniques within individual smart meters to forecast commodity consumption, such as electricity, gas, network bandwidth, or water, by analyzing consumption data and user information, and continuously updating the models based on new data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional aggregate-level forecasting methods are used, then the forecasting process is simple, but the forecast accuracy at individual meter level deteriorates
Solution Approach 1:
The patent segments the forecasting approach by implementing individual meter-level models rather than aggregate-level models. Each smart meter maintains its own machine learning model that is trained on local consumption data, allowing for precise forecasts at the individual meter level while capturing unique usage patterns and behaviors specific to each location.
Solution Approach 2:
The patent applies local quality by customizing the forecasting model to each individual meter's specific characteristics. The machine learning model is trained on local consumption data from each meter, incorporating location-specific factors such as usage patterns, appliance preferences, and occupancy fluctuations, thereby achieving high accuracy tailored to each meter's unique context.
2Measurement precision
If machine learning models are implemented at individual meters, then forecast accuracy improves, but computational resources and device complexity increase
Solution Approach 1:
The patent applies partial action by implementing machine learning models with appropriate complexity levels for each individual meter rather than using overly complex models. The models are designed to process consumption data efficiently, using only the necessary computational resources to achieve accurate forecasts while avoiding excessive energy consumption that would be required for more complex aggregate-level modeling approaches.
3Adaptability or versatility
If traditional forecasting methods are used, then the system is easier to implement, but adaptability to evolving consumption behaviors deteriorates
Solution Approach 1:
The patent applies dynamics by implementing machine learning models that continuously learn from new consumption data. The models are trained on historical data and automatically update their predictions based on evolving consumption patterns, occupancy changes, and external factors, enabling dynamic adaptation to changing behaviors without requiring manual reconfiguration of the forecasting system.
Solution Approach 2:
The patent implements feedback mechanisms where consumption data from smart meters is continuously fed back into the machine learning models for retraining. This feedback loop enables the models to learn from actual consumption behavior and improve their forecast accuracy over time, adapting to evolving patterns while maintaining system implementation through automated processes.
Data Source
AI summary
Various embodiments disclosed herein provide techniques for forecasting commodity consumption at the individual meter level. In various embodiments, a method includes receiving, by a metering device, data associated with consumption of a commodity by a plurality of consumption devices at a location. The method also includes determining, by the metering device, a number of users at the location. Also, the method includes generating, by the metering device using a machine learning model, a forecast of future consumption of the commodity based on the data associated with the consumption of the commodity and the number of users at the location, wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity monitored by the metering device.