Building Plant Operation Timing With 15-Minute Energy Forecasting
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Solution Overview
Problem
Current energy use analysis tools in commercial buildings are ineffective in accurately predicting energy use post-occupation, as they rely on generalized parameters and fail to account for unique building characteristics and external temperature variations.
Innovation Solution
A computer-implemented method that determines a building's natural thermal lag using linear regression, generates a best-fit regression model for energy forecasting, and optimizes heating and cooling plant operations based on 15-minute interval data and weather forecasts to improve energy usage forecasting and plant efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generalized parameters and tables are used for energy use analysis, then the analysis process is simplified and easier to perform, but the predictive accuracy of energy use post-occupation deteriorates
Solution Approach 1:
The patent transitions from using generalized parameters applicable to all buildings to building-specific parameters that capture unique characteristics of each building. This includes determining individual thermal lag values, occupancy patterns, and operational schedules for each building, thereby improving predictive accuracy while maintaining ease of use through automated data collection and processing.
Solution Approach 2:
The patent implements a commissioning phase where building-specific parameters are measured and stored before the building enters normal operation. This preliminary data collection includes thermal lag determination, occupancy pattern analysis, and operational schedule documentation, which are then used to create accurate energy use predictions without requiring complex real-time adjustments.
2Measurement precision
If sub-hourly energy forecasting is implemented, then the accuracy of energy usage prediction is improved, but the complexity of the forecasting system increases
Solution Approach 1:
The patent divides the forecasting system into distinct functional modules: data collection module, thermal lag determination module, occupancy pattern analysis module, and prediction calculation module. Each module handles a specific aspect of the forecasting process, making the overall complex system more manageable and easier to implement while achieving sub-hourly prediction accuracy.
Solution Approach 2:
The system automatically collects and processes building-specific data without requiring manual input or complex configuration. It self-determines thermal lag values, automatically analyzes occupancy patterns, and generates predictions based on collected data, thereby reducing operational complexity while maintaining high accuracy.
3Measurement precision
If building-specific parameters are determined and stored, then the predictive accuracy for individual buildings is improved, but the time and resources required for data collection increase
Solution Approach 1:
The patent performs data collection and parameter determination during the building commissioning phase, before the building enters normal operation. This preliminary action includes measuring thermal lag, documenting occupancy patterns, and recording operational schedules, thereby capturing essential building-specific parameters without disrupting future operations or requiring ongoing time investment.
Solution Approach 2:
The system continuously monitors actual energy consumption and compares it with predicted values, using the differences to refine and update building-specific parameters over time. This feedback mechanism allows the system to improve accuracy progressively while minimizing the initial time investment required for data collection.
Data Source
AI summary
The invention provides a method for improved building energy usage reduction by computer automation of optimized plant operation times and sub-hourly building energy forecasting to determine plant faults. The invention provides a computer system to derive the NTL, mechanical heat-up (MHL) and mechanical cool-down (MCL) lags and in conjunction with a readily available interval weather forecast, the system can output various signals to indicate optimized start and stop times for heating and cooling equipment. The algorithm to calculate the 15-minute energy forecast is used to indicate out-of-control conditions in the operation of the plant.