Deep Learning Power Load Probability Density Prediction
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Solution Overview
Problem
Current technologies lack an effective method for predicting the probability distribution of power load, which is crucial for the normal operation of power grid systems, and existing methods do not adequately account for factors like meteorological and air quality data that influence electricity consumption.
Innovation Solution
A deep learning-based method and system that collects historical power load, meteorological, and air quality data, divides it into training and test sets, determines a deep learning model, and uses kernel density estimation to predict power load probability density at different quantile points, improving prediction accuracy by considering weather and air quality conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time series methods or machine learning methods are used for power load prediction, then the prediction process is simpler, but the prediction accuracy is lower
Solution Approach 1:
The patent replaces traditional time series methods and machine learning methods with deep learning methods. Specifically, it uses a deep neural network model with multiple hidden layers to process power load data, meteorological data, and air quality data, achieving higher prediction accuracy through the substitution of more advanced computational mechanisms.
Solution Approach 2:
The patent combines multiple types of data (power load data, meteorological data, and air quality data) into a composite input for the deep learning model. This multi-source data integration creates a more comprehensive prediction system that captures various影响因素 of power consumption, thereby improving prediction accuracy.
2Measurement precision
If only power load data is used for prediction, then the data processing is simpler, but the prediction accuracy is lower
Solution Approach 1:
The patent merges multiple data sources including power load data, meteorological data (temperature, humidity, wind speed), and air quality data (PM2.5, PM10, SO2, NO2, CO, O3) into a unified prediction framework. This combination of diverse data types provides a more comprehensive view of factors influencing power consumption.
Solution Approach 2:
The deep learning model is designed to process multiple types of input data simultaneously, making it a multi-functional system that can handle various data formats and sources. The model serves multiple purposes by integrating different data types to predict power load probability density at different quantile points.
3Reliability
If probability distribution prediction is not implemented, then the prediction process is simpler, but the operation of power grid system cannot be ensured
Solution Approach 1:
The patent employs kernel density estimation to transform the predicted power load values into a probability density function. This mathematical transformation substitutes direct point prediction with probabilistic distribution prediction, providing more reliable information for power grid operation and risk assessment.
Data Source
AI summary
The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S101, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set; S102, determining a deep learning model for predicting power load; S103, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval; S104, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval.