Predictive Model for Transformer Level Energy Demand Forecasting
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Solution Overview
Problem
Current techniques for predicting energy workloads in electrical grids are inaccurate, leading to commercial losses due to inefficiencies in identifying peak demands and adjusting energy supply, especially at the transformer level, where changes cannot be made quickly enough to meet short-term demands.
Innovation Solution
A computer-implemented method and system that uses historical data and dynamic input parameters to develop a predictive model for short-term energy demand forecasting, allowing for dynamic configuration of control limits at the transformer level to optimize energy output and minimize costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current techniques are used for predicting energy workloads, then the system operates with existing methods, but the accuracy of identifying peak demands and adjusting energy supply deteriorates, leading to commercial losses
Solution Approach 1:
The system performs preliminary actions by developing a predictive model using historical data and dynamic input parameters before peak demands occur. This allows the system to forecast short-term energy demands and identify predicted peak values in advance, enabling proactive adjustment of energy supply to transformers rather than reactive responses after losses have occurred.
Solution Approach 2:
The system implements feedback mechanisms by continuously receiving dynamic input parameters and historical data, comparing predicted values with actual outcomes, and refining the predictive model over time. This feedback loop enhances the accuracy of energy demand profiling and allows the system to adapt to changing patterns in energy consumption.
2Speed
If transformer level adjustments are made quickly to meet short-term demands, then the responsiveness to peak demands improves, but the complexity of controlling the electrical grid increases
Solution Approach 1:
The system introduces an intermediary predictive model that acts as a mediator between historical data/input parameters and transformer control decisions. This model processes complex data relationships and translates them into actionable predictions, simplifying the control process while enabling rapid response to short-term energy demands without directly managing the full complexity of the electrical grid.
3Measurement precision
If more data and constraints are considered in the predictive model, then the accuracy of energy demand profiling improves, but the complexity of the model increases
Solution Approach 1:
The system segments the predictive modeling process by developing the model at a specific hierarchical level (transformer level or sub-Transformer level) rather than attempting to model the entire electrical grid simultaneously. This segmentation allows the inclusion of relevant local data and constraints without being overwhelmed by system-wide complexity, maintaining high accuracy for local energy demand predictions.
Data Source
AI summary
A model is generating for predicting energy workloads to adjust electrical energy supply to meet varying short-term energy demands at a microcosm level. A model is developed, using a computer, to facilitate predicting energy workloads for adjusting energy supplies to meet an energy demand. The model includes receiving, at the computer, input parameters of dynamic values of workloads as historical data, and generating a predictive model by analyzing the input parameters. The model further includes predicting short-term energy demands based on the predictive model, the predicted short-term energy demands include identifying a predicted peak value. Also, the model includes initiating short term energy output in an electrical grid to a transformer level component in the electrical grid based on the predicted short term energy demands.