Load Forecast In Ac Network TRIZ Cases
- Microgrid RL Agent Consensus for Wildfire Outage Control
Localized RL agent clusters use spatiotemporal consensus to cut outage response overhead and keep microgrid power flowing during wildfire events.
- Power Grid State Estimation Using Subsection Consensus
Partitioned grid sections use local unscented Kalman estimates and boundary consensus to deliver near real-time state estimation.
- Forward Energy Purchasing for Cost-Stable Machine Operation
AI forecasting and forward market purchases help machines hedge energy and compute volatility, lowering costs and improving resource use.
- Machine Resource Forward Purchasing for Ledger Energy Volatility
Intelligent agents pre-acquire energy and compute in forward markets to handle ledger volatility while reducing transaction energy use.
- Generator Load Bank Control for Fluctuating Drill Rig Loads
A controlled load bank keeps drill rig generators near constant load, reducing power swings and blackout risk during intermittent equipment use.
- Fleet Resource Transactions for Volatile Energy and Compute Markets
Aggregated fleet transactions use AI-driven resource allocation to balance compute, networking, and energy demand under volatile market conditions.
- Peer-to-Peer Smart Sockets for TCL Peak Load Smoothing
Distributed smart sockets forecast TCL demand locally and coordinate switching to smooth power peaks while preserving privacy.
- Community Energy Management With MILP-MPC and Load Forecasting
MILP-MPC control combines load monitoring and robust forecasting to improve community energy sharing across homes with different demand, generation, and storage.
- Non-Invasive Power Device Identification via Motif Analysis
A power analyzing device classifies similar power patterns to acquire motif information for identifying individual devices in a circuit.
- Composite Power-Data Cable Micro Grids for 5G Infrastructure
A composite power-data cable micro grid distributes electricity and signals to remote nodes via splice enclosures.
- Machine Learning Engine for Electric Power Event Location
A machine learning engine determines electric power event locations using trained topology matrices and logistic regression models.
- Central Plant Optimization via Streamlined Data Linkage
A central plant optimization system links design models with timeseries energy load data to determine optimal equipment allocation.
- Predictive Grid Control for Microgrid Stability
A predictive grid control system generates dynamic droop coefficients to manage asset operation.
- Three-Phase Compensation Circuit for Power Continuity
A three-phase compensation circuit redistributes current from functioning phases to replace malfunctioning ones.
- Utility Grid Appliance Control via Presence Detection
A utility server detects user presence to dynamically adjust appliance operational states and power delivery.
- Distributed Power Network with Autonomous Control Devices
A distributed electric power system uses control devices to manage energy distribution among multiple suppliers and demanders.
- Graphics Processor Power Prediction via Leakage and Switching Estimation
A computing device estimates graphics processor power consumption using leakage and switching data for real-time performance monitoring.
- Building Energy Lag Optimization for Brownout Prediction
A building lag optimizer generates time-shifted energy consumption data to detect thermal lag parameters through regression analysis.
- Microcontroller Power Manager Optimizing Voltage and Frequency
A microcontroller power manager adjusts output voltages and switching frequencies across multiple supplies to minimize energy loss.
- Storage Battery Control Device for Surplus Power Management
A storage battery control device manages charge states using load and photovoltaic power predictions to optimize energy utilization.
- Outage Prediction Using Grid Segmentation and ML
Grid segmentation and machine learning models predict outage locations to resolve reliance on guesswork during severe storms.
- Submodular Load Clustering with Robust PCA
Robust principal component analysis decomposes load profiles into low-rank and sparse components for submodular clustering.
- Energy Consumption Prediction via Data Decomposition
Segmenting granular consumption data into trend and seasonal components reduces training time while improving prediction precision for real-time monitoring.
- Generator Load Prediction Using External Sensor Data
A generator controller analyzes external sensor data to predict load events and adjust system configuration.
- Dynamic Virtual Model for Electrical System Predictive Analytics
A virtual system modeling engine synchronizes with real-time sensor data to generate accurate predictive analytics for electrical power infrastructure.