A Novel Forecasting Model for Solar Power Generation by a Deep Learning Framework with Data Preprocessing and Postprocessing

Phan, Quoc-Thang; Wu, Yuan‐Kang; Dzung, Phan Quoc; Lo, Hsin-Yen

doi:10.1109/icps54075.2022.9773862

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…To examine the correlation of meteorological factors with wind power generation at each time step, the Pearson correlation coefficient (PCC) was used as follows [25].…”

Section: Data Preprocessingmentioning

confidence: 99%

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Nguyen

Dzung

et al. 2023

Energies

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Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model.

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“…To examine the correlation of meteorological factors with wind power generation at each time step, the Pearson correlation coefficient (PCC) was used as follows [25].…”

Section: Data Preprocessingmentioning

confidence: 99%

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Nguyen

Dzung

et al. 2023

Energies

Self Cite

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“…They used postprocessing technique and GRU to enhance forecasting accuracy. [ 20 ] Sherozbek et al showed that transformers models are efficient for nontransparent and transparent solar panels with lower error rates in forecasting time series data. [ 21 ] Finally, Bosma and Nazari proposed using computer vision on weather maps to predict power production.…”

Section: Introductionmentioning

confidence: 99%

Time Series Prediction of Solar Power Generation Using Trend Decomposition

Kavakci,

Cicekdag,

Ertekin

2023

Energy Tech

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High‐accuracy predictions of future solar power generations are important for monitoring, maintenance, dispatching, and scheduling. The goal of this study is to create a forecasting workflow that increases prediction accuracy independent of the machine learning method and has minimal computational requirements. The proposed trend decomposition method incorporates irradiance and seasonal features as exogenous inputs. In order to extract the linear part of the data, a moving average filter is used. The nonlinear (stable) component of the time series is then calculated by subtracting this linear part from the original data. The stable portion is modeled using several machine learning methods, while the ordinary least squares method is applied to the linear series. By aggregating both results, the final forecast is obtained. The forecasting performances of the machine learning algorithms on unprocessed data are used as baselines for evaluations. Improvements up to 39% in the mean absolute error and up to 31% in the root mean square error metrics are observed compared to the baselines. Experimental results show that the proposed trend decomposition with extrapolation method increases the forecasting performance and generalization capacity of machine learning algorithms.

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Application of a new Transformer-based model and XGBoost to improve one-day-ahead solar power forecasts

Phan

Dzung

2023

2023 IEEE/IAS 59th Industrial and Commercial Power Systems Technical Conference (I&CPS)

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A Novel Forecasting Model for Solar Power Generation by a Deep Learning Framework with Data Preprocessing and Postprocessing

Cited by 5 publications

References 32 publications

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)

Time Series Prediction of Solar Power Generation Using Trend Decomposition

Application of a new Transformer-based model and XGBoost to improve one-day-ahead solar power forecasts

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