Energy Storage Scheduling for Accurate Building Load Prediction
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Solution Overview
Problem
Conventional energy management systems in buildings lack precision in predicting energy consumption and supply, particularly for devices with varying output based on weather conditions, leading to inefficient operation and potential energy imbalances.
Innovation Solution
An electric/thermal energy storage optimizing system that sets control settings for energy supplying, consuming, and storage devices, predicts energy consumption/supply based on past data, and optimizes start-stop schedules and energy storage using evaluation indices to enhance prediction accuracy and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional energy management systems use simple prediction methods based on past data, then the system complexity is low, but the prediction precision of energy consumption and supply is insufficient
Solution Approach 1:
The system segments the energy management prediction into multiple independent modules: weather condition analysis module, energy consumption prediction module, energy supply prediction module, and storage device control module. Each module handles specific aspects of the prediction, improving overall precision while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system performs preliminary weather condition analysis and energy pattern recognition before actual energy consumption and supply predictions. By pre-processing historical data and establishing baseline patterns, the system enhances prediction accuracy without requiring complex real-time calculations, thus balancing precision and computational complexity.
2Productivity
If the system optimizes start-stop schedules of control-target devices, then energy management efficiency improves, but the number of device operations increases leading to excessive start-stops
Solution Approach 1:
The system implements periodic operation schedules for control-target devices based on predicted energy patterns and weather conditions. Instead of frequent start-stop cycles, the optimizer determines optimal periodic operation intervals that maintain energy management efficiency while reducing the frequency of device启动和停止, thereby minimizing wear and time loss.
Solution Approach 2:
The system performs preliminary optimization calculations to determine the optimal start-stop schedule before actual device operation. By pre-calculating the best operation timing based on predicted energy prices and weather conditions, the system avoids excessive start-stops while maintaining high energy management efficiency.
3Loss of energy
If thermal energy storage devices are used for peak-cut, then peak time energy consumption decreases, but energy surplus during non-peak times increases
Solution Approach 1:
The system dynamically adjusts the charging and discharging schedules of thermal energy storage devices based on real-time weather conditions, predicted energy prices, and actual consumption patterns. This dynamic optimization ensures that energy is stored during periods of surplus and discharged during peak demand, balancing peak-cut benefits with minimizing overall energy surplus accumulation.
Solution Approach 2:
The system implements feedback mechanisms that monitor actual energy consumption and storage levels, continuously adjusting the optimization strategy. When energy surplus is detected during non-peak times, the system modifies charging schedules to prevent excessive surplus, while maintaining effective peak-cut performance through real-time feedback from consumption and storage data.
Data Source
AI summary
An electric/thermal energy storage schedule optimizing device is provided. The electric/thermal energy storage schedule optimizing device includes a predicting unit setting predicted values of a consumed energy or a supplying energy of a plurality of control-target devices. The electric/thermal energy storage schedule optimizing device also includes a start-stop optimizing unit creating start-stop schedules of the plurality of control-target devices based on the predicted values set by the predicting unit.