Multi-Energy Load Forecasting via Temporal Convolutional Networks
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Solution Overview
Problem
Existing load forecasting methods for integrated energy systems (IESs) are inadequate for multi-energy short-term forecasting, failing to accurately consider environmental factors and energy coupling, leading to inaccurate predictions.
Innovation Solution
A multi-energy integrated short-term load forecasting method using an encoder-decoder model with temporal convolutional networks and multi-head self-attention mechanism, incorporating rotary position embedding, to preprocess and classify data, mine coupling features, and perform accurate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-energy load forecasting methods are used, then the forecasting process is simple, but the coupling features between different energy loads cannot be mined, leading to inaccurate forecasting in integrated energy systems
Solution Approach 1:
The patent combines multiple single-energy forecasting models into a unified multi-energy forecasting model that processes electricity, gas, and water load data simultaneously. The model integrates coupling features between different energy types through shared encoder-decoder structures and cross-attention mechanisms, enabling accurate multi-energy load forecasting while maintaining computational efficiency.
2Adaptability or versatility
If existing multi-energy forecasting methods are used, then multiple energy loads are considered, but environmental factors on the energy consumption side are not comprehensively considered, resulting in insufficient forecasting accuracy
Solution Approach 1:
The patent performs preliminary data processing to collect and preprocess environmental factors (temperature, humidity, wind speed, etc.) before feeding them into the forecasting model. The encoder module pre-processes multi-energy load data and environmental data, extracting relevant features that are then used by the decoder for accurate forecasting, ensuring environmental factors are comprehensively considered from the outset.
3Device complexity
If ultra-short-term load forecasting methods (such as RNN and LSTM) are used, then the model structure is optimized, but the different sequence length requirements between short-term and ultra-short-term forecasting cannot be addressed, making them inapplicable to short-term forecasting
Solution Approach 1:
The patent employs a dynamic encoder-decoder architecture that can adapt to different forecasting time scales. The model uses variable sequence length processing in the encoder to handle both short-term and ultra-short-term requirements, with the decoder dynamically adjusting its prediction horizon based on the input data characteristics, making the same model structure applicable to multiple forecasting periods.
4Measurement precision
If multi-energy integrated short-term load forecasting is performed, then accurate forecasting can be achieved, but the data processing and model training require significant time and computational resources
Solution Approach 1:
The patent performs comprehensive data preprocessing including missing value imputation, normalization, and feature engineering before model training. The encoder module pre-extracts features from multi-energy load data and environmental data, creating compact representations that reduce the computational burden during training and inference, thereby reducing overall processing time while maintaining forecasting accuracy.
Data Source
AI summary
The disclosure provides a multi-energy integrated short-term load forecasting method and system, which relates to the technical field of load forecasting. In the disclosure, after classifying the acquired relevant data of multi-energy integrated short-term load forecasting, the data after sample classification is used to train the multi-energy integrated short-term load forecasting model. The model is composed of multiple layers of temporal convolutional networks having multi-head self-attention mechanism and rotary position embedding. Finally, the trained model is used to carry out the multi-energy integrated short-term load forecasting. The disclosure can fully mine the coupling feature between multi-energy loads, improve the accuracy of multi-energy integrated short-term load forecasting, and further improve the management level and service efficiency of integrated energy demand side.