Predictive Demand Shedding for Peak Load Energy Management
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Solution Overview
Problem
Conventional energy management systems are reactive and fail to proactively determine demand shedding events based on predicted peak loads, leading to inefficiencies in reducing peak energy consumption.
Innovation Solution
A system employing a machine learning model that combines long-term and short-term predicting modules, along with an outlier-detection module, to forecast energy load profiles and identify demand shedding time slots, enabling proactive energy management by adjusting device settings or energy sources during predicted peak periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional reactive demand shedding techniques are used, then energy consumption can be reduced, but the timing of demand shedding cannot be optimized
Solution Approach 1:
The system performs preliminary action by using machine learning models to predict peak load periods in advance before they occur. The long-term and short-term predicting modules forecast energy load profiles, allowing the system to proactively schedule demand shedding events during predicted peak periods rather than reacting after peaks are detected.
2Loss of time
If proactive demand shedding prediction is implemented, then timing of demand shedding is optimized, but system complexity increases
Solution Approach 1:
The prediction system is segmented into distinct functional modules: a long-term predicting module for forecasting overall energy load profiles, a short-term predicting module for near-term predictions, and an outlier-detection module for identifying anomalies. This modular segmentation manages complexity by dividing the predictive function into specialized components that can be developed and maintained independently.
Solution Approach 2:
The machine learning model acts as an intermediary between raw energy consumption data and demand shedding decisions. The model processes historical and real-time energy data, weather forecasts, and sensor inputs to generate predicted energy load profiles, translating complex multi-source data into actionable predictions that guide demand shedding scheduling.
3Productivity
If machine learning prediction modules are used, then demand shedding timing is improved, but computational requirements increase
Solution Approach 1:
The system applies partial action by using different prediction granularities for different time horizons. The long-term module provides coarse-grained forecasts for scheduling purposes, while the short-term module provides finer-grained predictions only for near-term optimization. This selective prediction approach reduces overall computational burden compared to continuous high-resolution forecasting.
Data Source
AI summary
A method including determining, via a machine learning model, a predicted energy load profile for a facility based at least in part on weather forecast data and sensor data for the facility. The sensor data can be received from one or more energy monitoring sensors for one or more devices in the facility. The method further can include determining one or more demand shedding time slots based at least in part on peak periods and the predicted energy load profile. Moreover, the method can include determining one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots. The method additionally can include causing a respective performance of each of the one or more demand shedding events by the one or more devices during the one or more demand shedding time slots. Other embodiments are disclosed.