Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks

Alcántara, Antonio; Galván, Inés María; Aler-Mur, Ricardo

doi:10.1016/j.engappai.2022.105128

Cited by 16 publications

(3 citation statements)

References 29 publications

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“…The PICP [27] represents the probability of the actual observation falling within the prediction interval, indicating interval reliability. When the PICP closely matches the confidence level, it signifies a higher predictive reliability.…”

Section: • Picpmentioning

confidence: 99%

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Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Lyu,

Shen,

Zhao

et al. 2023

Sustainability

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Solar energy, as a clean energy source, has tremendous potential for utilization. The advancement of solar energy utilization technology has led to an increasing demand for solar energy, resulting in a growing need for the accurate prediction of solar radiation. The main objective of this study is to develop a novel model for predicting solar radiation intervals, in order to obtain accurate and high-quality predictions. In this study, the daily sunshine duration (SD), average relative humidity (RHU), and daily average temperature (AT) were selected as the indicators affecting the daily global solar radiation (DGSR). The empirical study conducted in this research utilized daily solar radiation data and daily meteorological data collected at the Hami station in Xinjiang from January 2009 to December 2016. In this study, a novel solar radiation interval prediction model was developed based on the concept of “point prediction + interval prediction”. The Conformer model was employed for the point prediction of solar radiation, while the Generalized Laplace (GLaplace) distribution was chosen as the prior distribution to account for the prediction error. Furthermore, the Solar DeepAR Forecasting (SDAR) model was utilized to estimate parameters of the fitted residual distribution and achieve the interval prediction of solar radiation. The results showed that both models performed well, with the Conformer model achieving a Mean Squared Error (MSE) of 0.8645, a Mean Absolute Error (MAE) of 0.7033 and the fitting coefficient R2 of 0.7751, while the SDAR model demonstrated a Coverage Width-based Criterion (CWC) value of 0.068. Compared to other conventional interval prediction methods, our study’s model exhibited superior accuracy and provided a more reliable solar radiation prediction interval, offering valuable information for ensuring power system safety and stability.

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Section: • Picpmentioning

confidence: 99%

“…The PINAW [27] represents the average width of normalized prediction intervals, serving as a measure to assess interval prediction accuracy and effectiveness. The formula for calculating PINAW is shown below:…”

Section: • Picpmentioning

confidence: 99%

Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Lyu,

Shen,

Zhao

et al. 2023

Sustainability

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“…Compared with the conventional ANN with one hidden layer, Amasyali et al found that higher accuracy can be achieved by using the Deep Neural Networks (DNN) with deeper architectures at the same sample size (Amasyali and El-Gohary, 2021). Alcantara et al employed the DNN algorithm based on the hypernetworks method, enabling the attainment of prediction intervals with optimal coverage width for solar and wind energy (Alcantara et al, 2022). Parizad and Hatziadoniu adopted the random search algorithm to confine the search range of hyperparameters within a local region.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based correlation analysis for probabilistic power flow considering renewable energy and energy storage

Xia,

Xiao,

2024

Front. Energy Res.

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Developing photovoltaic (PV) and wind power is one of the most efficient approaches to reduce carbon emissions. Accumulating the PV and wind energy resources at different geographical locations can minimize total power output variance as injected into the power systems. To some extent, a low degree of the variance amplitude of the renewable resources can reduce the requirement of in-depth regulation and dispatch for the fossil fuel-based thermal power plants. Such an issue can alternatively reduce carbon emissions. Thus, the correlation problem by minimizing the variance of total PV and wind power plays a vital role in power system planning and operation. However, the synergistic effect of power output correlation is mainly considered on the generation side, and it is often neglected for the correlation relationship between the power grid components. To address this problem, this paper proposes a correlation coefficient analysis method for the power grid, which can quantify the relationship between energy storage and the probabilistic power flow (PPF) of the grid. Subsequently, to accelerate the mapping efficiency of power correlation coefficients, a novel deep neural network (DNN) optimized by multi-task learning and attention mechanism (MA-DNN) is developed to predict power flow fluctuations. Finally, the simulation results show that in IEEE 9-bus and IEEE14-bus systems, the strong correlation grouping percentage between the power correlation coefficients and power flow fluctuations reached 92% and 51%, respectively. The percentages of groups indicating weak correlation are 4% and 38%. In the modified IEEE 23-bus system, the computational accuracy of MA-DNN is improved by 37.35% compared to the PPF based on Latin hypercube sampling. Additionally, the MA-DNN regression prediction model exhibits a substantial improvement in assessing power flow fluctuations in the power grid, achieving a speed enhancement of 758.85 times compared to the conventional probability power flow algorithms. These findings provide the rapid selection of the grid access point with the minimum power flow fluctuations.

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