Application of Deep Learning to the Prediction of Solar Irradiance through Missing Data

Abstract

The task of predicting solar irradiance is critical in the development of renewable energy sources. This research is aimed at predicting the photovoltaic plant’s irradiance or power and serving as a standard for grid stability. In practical situations, missing data can drastically diminish prediction precision. Meanwhile, it is tough to pick an appropriate imputation approach before modeling because of not knowing the distribution of datasets. Furthermore, not all datasets benefit equally from using the same imputation technique. This research suggests utilizing a recurrent neural network (RNN) equipped with an adaptive neural imputation module (ANIM) to estimate direct solar irradiance when some data is missing. Without imputed information, the typical projects’ imminent 4-hour irradiance depends on gaps in antique climatic and irradiation records. The projected model is evaluated on the widely available information by simulating missing data in each input series. The performance model is assessed alternative imputation techniques under a range of missing rates and input parameters. The outcomes prove that the suggested methods perform better than competing strategies when measured by various criteria. Moreover, combine the methodology with the attentive mechanism and invent that it excels in low-light conditions.

Examining the Combination of a Parabolic Solar Collector with Phase Change Material (PCM) in Solar Distillation

“…This observation gives the exact comparison of using thermal storage unit with the single slope solar still integrated with the PSC. The productivity of this experiment gives almost 61% [3]. The DSSD coupled with relatively sun warmed solar pond (Shallow) showed the improved productivity than the others.…”

Weighted Average Ensemble-Based PV Forecasting in a Limited Environment with Missing Data of PV Power

“…Kim, T. et al [25] proposed the impact of missing weather information on PV forecasting performance was analyzed by applying imputation methods such as LI and MICE before developing the forecasting model, wherein the KNN-applied model outperformed other imputation-applied models. Girimurugan, R. et al [26] proposed an adaptive neural imputation module (ANIM) coupled with a recurrent neural network (RNN) and an attention imputed gate recurrent unit (AIGRU), which effectively utilizes the distribution of each weather variable during the imputation process, surpassing traditional methods like KNN in efficacy. Lastly, Lee, D. S. et al [27] devised a two-stage approach employing linear regression and KNN to address missing weather data in the development of short-and medium-term PV forecasting models, which yielded enhanced performance compared to models that did not incorporate these imputation techniques.…”

“…Previous studies have primarily focused on developing forecasting models and evaluating their performance after imputing missing PV data [24][25][26][27]. Our review identified that different forecasting performances emerged when single imputation methods were applied to PV data, influenced by the characteristics of the imputation method and weather conditions.…”