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The Error Correction Model displays better fitting accuracy and forecast precision than the MLR model.
Spurious regression may be observed while using an MLR model without testing the stationarity of the series, thus decreasing forecast precision.
The results indicate that the average error of model training is small, the forecast effect is good, and it can satisfy the request of forecast precision that engineering practice to comminuting productive rate.
However, the forecast precision of most existing ANN based forecast models has not been satisfactory to researchers and engineers so far, and the generalization capability of these networks needs further improving.
Some additional steps, e.g. applying historical information of cloud cover to sample data sets and the cloud cover from the weather forecast to network input, are adopted to help enhance the forecast precision.
However, a fact could not be neglected that most of such existing ANN-based models have not yet satisfied researchers and engineers in forecast precision so far, and the generalization capability of these networks needs further improving.
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It is depicted that including the utilization of limited energy usage and environmental data as one of the model's input variables, the electricity forecasting precision is more accurate, precise and can be improved.
In this study, we present the advantage of DWT to improve time series forecasting precision.
However, the application ranges and forecasting precision of these methods vary considerably.
Compared with the traditional model, TFRWν-SVM method requires fewer samples and has better forecasting precision.
The final results indicate that IEWT can effectively reduce the noise interference and enhance the forecasting precision of complex signal.
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