Generator Load Prediction Using External Sensor Data
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Solution Overview
Problem
Existing generator systems face inefficiencies in predicting and managing sudden changes in load demand, leading to unnecessary fuel and resource consumption due to the inability to detect load triggers before they occur, resulting in the need for maintaining excess generation capacity to handle unpredictable loads.
Innovation Solution
A generator system with a controller that utilizes sensors to collect and analyze data on electrical parameters, external factors, and environmental conditions to predict load events, allowing for the identification of useful triggers and adjusting generator configuration accordingly, thereby reducing the number of generators needed online.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional generator capacity is maintained online to handle unpredictable high load demands, then system reliability is improved, but fuel consumption and resource usage increase
Solution Approach 1:
The system performs preliminary actions by detecting load triggers before actual load events occur. The controller monitors external sensor data (temperature, humidity, occupancy, weather conditions) and identifies patterns that precede high load demands, allowing the generator to prepare and respond only when necessary, rather than maintaining constant excess capacity.
Solution Approach 2:
The system implements feedback by continuously monitoring both internal generator parameters and external environmental factors. The controller analyzes sensor data in real-time, compares it against learned patterns from historical load events, and adjusts generator operation accordingly. This closed-loop feedback enables dynamic optimization between reliability and fuel consumption.
2Loss of energy
If the generator system configuration is dynamically adjusted based on predicted load events, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs self-service through automated pattern recognition and decision-making. The controller independently analyzes sensor data, identifies load triggers using machine learning algorithms, and adjusts generator configuration without human intervention. This automation handles the complexity internally while presenting a simple interface for resource optimization.
Solution Approach 2:
The controller acts as an intermediary between external environmental factors and the generator system. It processes complex sensor data from multiple sources (temperature sensors, humidity sensors, occupancy detectors, weather stations) and translates these into appropriate generator control actions, shielding the physical system from the complexity of environmental monitoring.
3Measurement precision
If external sensor data is analyzed to identify load triggers, then prediction accuracy is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system segments the detection task by dividing it into distinct sensor types and data categories. External sensors are organized into specific groups (temperature sensors, humidity sensors, occupancy detectors, weather station data), each monitored independently. The controller processes these segmented data streams separately before integrating them for comprehensive load trigger analysis, making the complex detection task manageable.
Data Source
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AI summary
An apparatus for predicting a load event in a generator system or a method for predicting the load event in the generator system may include monitoring output data associated with an electrical output of a generator (11), monitoring external sensor (14) data associated with the generator (11), detecting a load event for the generator (11) from the output data, identifying a subset of the external sensor (14) data that preceded the load event from the output data, performing an analysis of the subset of the external sensor (14) data, and determining a load event characteristic from the analysis, the load event characteristic indicative of a subsequent load event for the generator (11).