Industrial Energy Optimization Platform for Forecasting and Scheduling
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Solution Overview
Problem
Current industrial energy systems in parks lack comprehensive dynamic optimization, with traditional platforms only providing real-time simulation and not addressing future operation trends or offering optimal scheduling suggestions, especially for integrated energy systems with multiple energy chains.
Innovation Solution
A distributed industrial energy operation optimization platform that automatically constructs intelligent models and algorithms, featuring a modeling terminal, background service, and human-computer interface, enabling edge-cloud cooperation for real-time data processing, forecasting, and adaptive scheduling across multiple energy systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulation platforms are used for industrial energy systems, then real-time simulation capability is provided, but future operation trends cannot be predicted and optimal scheduling suggestions are not offered
Solution Approach 1:
The platform segments the energy system into multiple independent energy chains (power, heat, cold, gas) that can be modeled and optimized separately but coordinated together. This segmentation allows the system to handle complex multi-energy systems through modular intelligent models, improving both prediction accuracy and modeling efficiency.
Solution Approach 2:
The platform replaces traditional physics-based mechanical modeling with data-driven intelligent models (neural networks, fuzzy logic, expert systems). This substitution enables the system to capture complex non-linear relationships in energy systems without requiring detailed physical mechanisms, significantly improving modeling efficiency while maintaining prediction accuracy.
2Productivity
If data-driven methods are applied to model industrial energy systems, then modeling efficiency is improved, but system complexity increases due to multiple energy chains with different response time scales
Solution Approach 1:
The platform employs dynamic modeling approaches that adapt to different response time scales of various energy chains. Each energy chain is modeled with appropriate time constants and dynamics, allowing the system to handle fast-response electrical systems and slow-response thermal systems simultaneously. This dynamic approach manages system complexity while maintaining high modeling efficiency.
Solution Approach 2:
The platform develops universal intelligent models that can be applied across different energy chains (power, heat, cold, gas) despite their different characteristics. These multi-functional models use common data-driven methodologies and can be configured for specific energy types, reducing the overall system complexity while improving modeling efficiency through reuse and standardization.
3Loss of energy
If comprehensive energy optimization is implemented for integrated energy systems, then energy conservation and cost reduction are achieved, but the complexity of coordination across multiple energy chains increases
Solution Approach 1:
The platform merges multiple energy chains (power, heat, cold, gas) into a unified optimization framework that coordinates them together. By integrating these previously separate systems into a single comprehensive optimization model, the platform achieves synergistic energy efficiency improvements while managing coordination complexity through a unified control architecture.
Solution Approach 2:
The platform implements feedback mechanisms that continuously monitor the state of multiple energy chains and adjust operations to maintain optimal efficiency. Real-time feedback from sensors and system performance data enables dynamic coordination across energy chains, achieving comprehensive energy optimization while the feedback loop manages complexity by providing continuous system state information for coordinated control.
Data Source
AI summary
A distributed industrial energy operation optimization platform which is capable of automatically constructing intelligent models and algorithms, is divided into three parts: a modeling terminal, a background service and a human-computer interface. The models like data pre-processing, energy generation-consumption-storage trend forecasting and optimal scheduling decision models are encapsulated in the modeling terminal as different visualization modules facing with multiple categories production scenarios, by dragging which the complex functional models can be realized conveniently. The background service is capable of automatically constructing the training samples and the production plans/manufacturing signals series according to the device model requirements of each edge side, interacts with the trained intelligent models through corresponding interfaces, and the computing results are saved in the specified relational database. The computing results are displayed through a friendly customer human-computer interface, and the real-time state of current working condition can also be adjusted.