System and method for energy forecasting based on indoor and outdoor weather data
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Solution Overview
Problem
Current energy management systems lack real-time and accurate forecasting of utility usage and costs, particularly for households and businesses, as they rely on historical data and do not account for dynamic changes in heating and cooling demands and user behavior.
Innovation Solution
An integrated system that collects current and historical heating and cooling load data, combines it with weather data and user inputs, and uses a controller to predict energy usage and costs by analyzing device usage patterns, providing real-time and forecasted estimates through a feedback loop that learns from user habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional energy management systems use historical data only, then system complexity is reduced, but measurement precision and forecasting accuracy deteriorate
Solution Approach 1:
The patent combines multiple data sources (historical energy consumption data, real-time weather data, building characteristics) and multiple monitoring components (energy monitors, weather stations, building management systems) into an integrated forecasting system. This merging of diverse data streams and components enables accurate real-time and forecasted energy usage predictions while accounting for dynamic environmental and behavioral factors.
2Measurement precision
If the system collects comprehensive real-time data from multiple sources, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional controller that performs diverse functions: collecting data from multiple sources (energy monitors, weather stations, building systems), processing and analyzing the data, generating real-time and forecasted predictions, and providing user interfaces for interaction. This universal controller consolidates multiple specialized components into a single multi-functional unit, reducing overall system complexity while maintaining comprehensive data collection capabilities.
3Measurement precision
If the system analyzes individual device usage patterns, then measurement precision improves, but loss of time for data processing increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing energy consumption data as it is collected from various sources, organizing it into structured formats that facilitate rapid analysis. The system pre-calculates baseline consumption patterns and stores processed weather data, enabling quick retrieval and analysis during forecasting operations. This preliminary organization of data significantly reduces the time required for real-time device-level usage analysis.
4Productivity
If the system provides real-time forecasting capabilities, then productivity improves, but use of energy by the system increases
Solution Approach 1:
The patent implements periodic action by updating energy forecasts at optimized intervals rather than continuously. The system performs real-time forecasting at strategically determined moments based on significant changes in weather conditions, building occupancy patterns, or energy consumption anomalies. This periodic updating approach provides timely energy optimization guidance while minimizing the computational energy required compared to continuous real-time processing.
Data Source
AI summary
An integrated system and method measures building characteristics and user behavior to provide real-time and forecasted utility usages and costs. The system gathers current and historical heating and cooling load data, compares the data with current and historical weather data and a building system set point, and calculates the heating or cooling load needed for the building based on the user's call for heat or cooling and the ambient environmental conditions. The system additionally analyzes individual device usage using usage signatures and user inputted tracking to create a comprehensive real-time and forecast of utility usages with the estimated costs. Through history of selections with usage changes corresponding to user input of individual devices, the system will be able to learn various devices' usage. The system then creates a comprehensive, real-time forecast of utility costs including the foregoing characteristics.