Abstract
With the continuous development of global science and technology industry, the demand for power is increasing, so short-term power load forecasting is particularly important. At present, a large number of load forecasting models have been applied to short-term load forecasting, but most of them ignore the error accumulation in the iterative training process. To solve this problem, this article proposes a combined measurement model which combines stacked bidirectional gated recurrent unit (SBiGRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and error correction. In the first stage, SBiGRU model is established to study the time series characteristics of load series under the influence of temperature and holiday types. The error series generated in the prediction process of SBiGRU model reflects the error characteristics of load series; In the second stage, the error sequence is decomposed into several intrinsic mode functions (IMF) components and trend components by CEEMDAN algorithm. The SBiGRU model is established again for each component to learn and predict, and the predicted values of all components are reconstructed to get the error prediction results; Finally, sum the two-stage prediction results to correct the error. The accuracy of SBiGRU-CEEMDAN-SBiGRU combination model is evaluated by two public power load data. The experimental results show that the SBiGRU-CEEMDAN-SBiGRU combination model has better accuracy and stability than the traditional model.
Highlights
Short term load forecasting is to estimate and forecast the electricity demand in the 24 hours, days or weeks on the premise of considering the influence of meteorological factors and working day types
The three indexes in formula (24) ∼ (26) are compared, as shown in Tab.5. It can be seen from Tab.5 that the SVR model performs the worst with an average daily accuracy of 97.34%; Long short-term memory (LSTM) records the historical information of load series in the process of forecasting, and the prediction accuracy is 97.82%; the stacked bidirectional gated recurrent unit (SBiGRU) model takes into account the data information of past and future times in the prediction process, and uses the 2 + 1 layer network structure to improve the generalization ability of the model, with an accuracy rate of 98.26%; SBiGRU-SBiGRU uses SBiGRU as the error correction model, so the prediction accuracy is improved, and the daily average accuracy rate is 98.58%; The SBiGRUCEEMDAN-SBiGRU model uses CEEMDAN algorithm to decompose the error series and can predict the error series on different time scales
Tab.6 shows the time calculation cost of different models. It can be seen from the table that the training and prediction time of SBiGRU-CEEMDAN-SBiGRU model is much longer than that of other models, indicating that the model improves the prediction accuracy at the expense of calculation cost
Summary
Short term load forecasting is to estimate and forecast the electricity demand in the 24 hours, days or weeks on the premise of considering the influence of meteorological factors and working day types. The standard GRU often ignores the context information in the load series, and unable to effectively capture the time sequence rules in the load series To solve this problem, the bidirectional RNN network model based on LSTM and GRU is used in the literature [21] to predict the short-term power load, and it is verified on two data sets. The algorithm uses SBiGRU to learn the main characteristics of load data, and the error information is reflected in the error sequence predicted by SBiGRU model; secondly, it uses CEEMDAN-SBiGRU model to fit the error series; SBiGRU model and CEEMDANSBiGRU model are combined to obtain the two-stage prediction model in this article. The specific calculation process is shown in formula (1)∼(4)
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
