A dual decomposition with error correction strategy based improved hybrid deep learning model to forecast solar irradiance

ABSTRACT

An accurate forecasting of solar irradiation plays a vital role in grid balancing, scheduling, and maintenance. With this aim, a deep learning solar forecasting model based on dual decomposition with error correction strategy is proposed in this paper. In this model, historical time series of solar irradiance and error series are decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD), respectively. The bidirectional long short-term memory (BiLSTM) deep learning (DL) network is used with each series to extract the different long- and short-term characteristics of the data. To observe the forecasting accuracy of the proposed model, nine contrast models are built to estimate one to three steps ahead solar irradiance for two different locations. The experimental results demonstrate that the proposed model achieved lower RMSE (1.81 W/m²–28.46 W/m²) and MAPE (0.2–4.40%) compared to the considered models. It improves the RMSE (17.55–72.82%) and MAPE (2.65–74.66%) over a non-error correction strategy-based hybrid model. In addition, the improvement in RMSE (13.82–65.10%) and MAPE (11.46–68.33%) are observed over single decomposition-based error correction model. Moreover, to validate the forecasting performance of the proposed model Diebold–Mariano Hypothesis (DMH) test, percentage improvement and its behavior in different weather conditions are also presented.

KEYWORDS:

• Bidirectional long short-term memory
• variational mode decomposition
• complete ensemble empirical mode decomposition with adaptive noise
• solar forecasting
• hyperparameters

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The codes of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research is not supported by any funding agency.