EV Charging Demand Prediction Model Using Weather and Failure Data
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Solution Overview
Problem
Existing technologies lack accurate prediction of electric vehicle charging demand due to the absence of consideration for charging facility failures and the impact of multiple climate data, leading to incomplete prediction systems and data pipeline designs.
Innovation Solution
A method is developed to establish a charging capacity prediction model based on meteorological factors and charging facility failures, involving data preprocessing, correlation analysis, noise reduction using Fast Fourier Transform, and training a prediction model with multilayer perception, convolutional neural networks, or long short-term memory models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction models are used without considering facility failures and multiple climate data, then the prediction system is simpler and easier to implement, but the prediction accuracy of electric vehicle charging demand is insufficient
Solution Approach 1:
The prediction system is segmented into multiple independent modules: data collection module (collecting charging data, meteorological data, and facility status data), data preprocessing module (cleaning and normalization), correlation analysis module (analyzing relationships between variables), and prediction model module (using machine learning algorithms). This segmentation allows each module to focus on specific tasks, improving overall prediction accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent merges multiple data sources (charging transaction data, meteorological data including temperature and precipitation, and facility status data) and multiple prediction models into a unified comprehensive prediction system. By combining these diverse elements, the system captures more影响因素 of charging demand, thereby improving prediction accuracy despite increased complexity.
2Reliability
If charging facility failures are not considered in the prediction model, then the model structure is simpler, but the prediction results do not reflect real-world charging availability accurately
Solution Approach 1:
The system performs preliminary analysis of facility status data to identify and characterize failure patterns before they affect charging demand predictions. By pre-processing and analyzing facility reliability data, the model proactively incorporates failure probabilities into the prediction framework, improving reliability without requiring complex real-time adjustments during prediction.
Solution Approach 2:
The patent introduces facility status data as an intermediary variable that mediates between raw charging data and final prediction results. This intermediary layer processes failure information and translates it into meaningful features for the prediction model, allowing the system to account for facility reliability without directly complicating the core prediction structure.
3Measurement precision
If multiple meteorological factors are not incorporated into the prediction, then the data processing is simpler and faster, but the impact of climate on charging demand is not captured
Solution Approach 1:
The system extracts specific relevant meteorological factors (temperature, precipitation, and other climate variables) from comprehensive weather data, focusing only on those factors that have significant correlation with charging demand. This extraction approach captures climate impacts on prediction accuracy while avoiding the complexity of processing all possible meteorological parameters.
Solution Approach 2:
The patent transforms raw meteorological data into meaningful predictive parameters by analyzing correlations between weather conditions and charging behavior. The system identifies and emphasizes key parameters (such as temperature thresholds or precipitation intensity levels) that most significantly influence charging demand, converting complex climate data into simplified yet effective prediction inputs.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method improves the accuracy of predicting electric vehicle charging demand, enhancing the planning and management of charging infrastructure by accounting for facility failures and climate impacts, thus supporting the deployment of electric vehicle charging networks.
Implementation Method 1
decomposing a time series of the charging capacity data, and performing a conversion to obtain time domain-based charging capacity time series data after reducing noise
Data Source
AI summary
Disclosed are a method of establishing a charging capacity prediction model based on meteorological factors and charging facility failures and its prediction method and system. The method includes: receiving charging capacity data of a charging facility and meteorological data of its location; extracting the number of random failures based on time from the charging capacity data, and obtaining the probability of failure from the probability mass function; performing a correlation test of the meteorological data with the charging capacity in the charging capacity data to obtain at least one feature factor; decomposing the time series of the charging data, and transforming to obtain the time series of charging data based on the time domain after noise reduction; and establishing a prediction model, using the probability values and feature factors as reference features, and using the charging time series data as a predictive target to train the prediction model.