Energy Forecasting Model With Random Forest and CNN-LSTM
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Solution Overview
Problem
Existing energy forecasting models often fail to accurately capture spatial and temporal patterns in data, leading to inaccurate predictions due to uneven variable weighting and lack of adaptability across different contexts.
Innovation Solution
A system that uses a random forest classifier to identify relevant variables, constructs a machine-learning model with CNN and LSTM networks, and trains it using historical data to generate precise energy consumption or production forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting models are used, then the model structure is simple, but the forecasting accuracy is low due to inability to capture spatial and temporal patterns
Solution Approach 1:
The model segments the forecasting task into two distinct components: a CNN component that captures spatial patterns from satellite imagery and a LSTM component that captures temporal patterns from historical data. This segmentation allows each component to specialize in specific pattern recognition, improving overall forecasting accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent combines two different neural network architectures (CNN and LSTM) into a hybrid model, similar to how composite materials combine different substances to achieve superior properties. The CNN-LSTM composite structure leverages the spatial feature extraction capability of CNN and the temporal sequence processing capability of LSTM to achieve forecasting accuracy that neither model could achieve alone.
2Productivity
If all available variables are used as inputs, then the data comprehensiveness is high, but the model efficiency decreases due to unnecessary data and uneven variable weighting
Solution Approach 1:
The random forest classifier extracts and identifies the most relevant input variables from the complete dataset by evaluating feature importance scores. This extraction process removes unnecessary variables and unevenly weighted features, retaining only the most informative inputs for the forecasting model, thereby improving efficiency without losing critical information.
Solution Approach 2:
The model applies different processing quality to different input variables through the random forest analysis, identifying which variables require detailed processing and which can be simplified or excluded. This local quality approach ensures that computational resources are focused on the most influential variables, improving overall model efficiency.
3Reliability
If the model is trained with comprehensive historical data, then the training completeness is high, but the training time increases
Solution Approach 1:
The random forest classifier extracts the most informative features from the comprehensive historical dataset, creating a reduced feature set that retains the essential patterns needed for reliable training. This extraction reduces the dimensionality of the training data, significantly decreasing training time while maintaining training completeness and model reliability.
Data Source
AI summary
In one example, a system can receive an input from a user indicating a target variable to be forecasted over a future time window. The system can then determine independent variables that influence the target variable and generate a set of candidate variables, including combinations of the independent variables. The system can then execute a random forest classifier to identify a subset of candidate variables having a threshold level of influence on the target variable. The system can then construct a machine-learning model configured to receive the identified subset of candidate variables as inputs and generate a forecast of the target variable. After constructing the machine-learning model, the system can train the machine-learning model using historical data and then execute the machine-learning model to generate the forecast.