Abstract
Multienergy load forecasting (MELF) is the premise of regional integrated energy systems (RIES) production planning and energy dispatch. The key of MELF is the consideration of multienergy coupling and the improvement of prediction accuracy. Therefore, a MELF method considering the multienergy coupling of variation characteristic curves (MELF_MECVCC) for RIES is proposed. The novelty of MELF_MECVCC lies in the following three aspects. 1) For the trend stripping and volatility extraction of multienergy load time series, the extreme-point symmetric mode decomposition-sample entropy (ESMD-SE) method is introduced to decompose and reconstruct the variation characteristic curves of multienergy, including trend curve and fluctuation curve. 2) The multienergy coupling of the variation characteristic curves is considered to reflect the variation characteristics of the multienergy loads. 3) Different methods are applied according to different variation characteristics; i.e., the combined method based on multitask learning and long short-term memory network (MTL-LSTM) is applied to predict the trend curve with strong correlation and the least square support vector regression (LSSVR) method is applied to predict the fluctuation curve with strong volatility and high complexity. Based on the actual data set of the University of Texas in Austin, the MELF_MECVCC model is simulated and verified, which shows that the model reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) and fits better with the original load and has higher prediction accuracy, compared with current advanced algorithms.
Highlights
The RIES utilize the advanced technology of physical information systems and innovative management models to integrate a variety of heterogeneous energy sources such as electricity, heating, and cooling in a certain area to realize coordinated planning, management, and optimized operation of diversified energy
Compared with the traditional single-energy systems, RIES integrate diversified forms of energy supply, conversion, and storage equipment, which improve the coupling of different types of energy in different links such as source, network, and load, and improve the Multienergy Load Forecasting flexibility of the overall system energy consumption (Shi et al, 2018)
In the experimental verification of the MELF-MECVCC model, the explanation is divided into two parts
Summary
The RIES utilize the advanced technology of physical information systems and innovative management models to integrate a variety of heterogeneous energy sources such as electricity, heating, and cooling in a certain area to realize coordinated planning, management, and optimized operation of diversified energy. Compared with the traditional single-energy systems, RIES integrate diversified forms of energy supply, conversion, and storage equipment, which improve the coupling of different types of energy in different links such as source, network, and load, and improve the Multienergy Load Forecasting flexibility of the overall system energy consumption (Shi et al, 2018). They are important parts of the new generation energy systems to build clean, low-carbon, safe, and efficient modern energy systems. At the same time, considering the coupling of diversified energy sources has become one of the key points of load forecasting
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call