Short-Term Load Forecasting in Power Distribution Networks
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Solution Overview
Problem
Current short-term load forecasting in power grids is imprecise, leading to potential brownouts or wasteful generation due to reliance on static load profiles and human intervention, which is time-consuming and error-prone, especially in distribution systems with high variability.
Innovation Solution
A system and method for short-term load forecasting that includes data acquisition, pre-processing using statistical techniques to rectify errors, and estimation of future power values based on historical and real-time data, balancing the load in the power distribution network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static load profiles based approaches are used to estimate short-term load demand, then the forecasting process is simple, but the forecasting precision is low
Solution Approach 1:
The patent transitions from static load profiles to dynamic load forecasting by incorporating real-time weather data, historical power consumption patterns, and time-varying parameters. The system continuously updates forecasts based on changing conditions, making the forecasting process adaptive rather than fixed, thereby improving precision while maintaining computational efficiency through automated statistical models.
Solution Approach 2:
The patent changes the parameters used in forecasting from fixed static profiles to dynamic parameters including weather conditions (temperature, humidity, wind speed), time of day, day of week, and historical consumption patterns. By varying these parameters based on real-time data, the system achieves higher forecasting precision without significantly increasing process complexity.
2Measurement precision
If human intervention is used to build and modify forecasts using historical power grid data, then the forecast accuracy can be improved, but the time consumption increases
Solution Approach 1:
The patent implements self-service forecasting through automated statistical models and machine learning algorithms that independently analyze historical data, identify patterns, and generate forecasts without human intervention. The system automatically adjusts to new data and refines its predictions, eliminating the time-consuming manual forecast building process while maintaining or improving accuracy through consistent data-driven decision making.
Solution Approach 2:
The patent incorporates feedback mechanisms where actual power consumption data is continuously compared with forecasted values, and the system automatically learns from these deviations to improve future predictions. This closed-loop approach enables the system to self-correct and enhance accuracy over time without requiring manual adjustments, thereby reducing time consumption while improving forecast precision.
3Reliability
If real-time load forecasting is implemented in distribution systems, then the reliability of power supply is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the load forecasting system into modular components: data acquisition modules for collecting weather and consumption data, statistical analysis modules for processing historical patterns, and forecast generation modules for producing predictions. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling real-time forecasting capabilities that improve power supply reliability through better load management.
Data Source
AI summary
A method for short term load forecasting in a power grid includes obtaining historical data comprising power data, load data and weather data corresponding to time index data recorded from a location in a power distribution network of the power grid. The method further includes receiving power grid data comprising a plurality of power values, and a plurality of weather parameter values corresponding to a plurality of recent time instant values. The method also includes generating modified historical data using statistical techniques to rectify error conditions. The method further includes estimating one or more power values at a future time instant based on the modified historical data and the power grid data. The method also includes balancing load of the power distribution network based on the estimated one or more power values.