A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

Abstract

The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.

Solar Radiation Forecasting — State of Art and Methodological Discussion

“…Such works can be referenced as reviews of techniques described in the literature with the presentation of their advantages and disadvantages [5,48]. Some articles consist of general or summarising discussions on selected aspects of solar forecasting in power systems and penetration of solar power generation with supporting in-depth reviews and citations [49][50][51]. The final list of publications includes synthesising and classifying works in solar forecasting.…”

Розробка Графічного Інтерфейсу Для Короткострокового Прогнозування Сонячної Генерації На Основі Глибокого Навчання

“…The input gate t i defines which data is stored in the new candidate state of the cell  t C [47,48]:…”

“…C are used to update the last state of the cell t C . This step is described by the expression (4) [47,48].…”

Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach

“…Wind power generation, due to its stochastic and intermittent nature, is heavily contingent on the variability of wind speed, making it one of the most challenging meteorological parameters to predict [8]. Forecasting models are categorized into short-term (10 min to 1 h), medium-term (1 h to 24 h), and long-term (1 day to 2 days) predictions, based on the temporal depth of the forecast, and into statistical, physical, and machine learning models, depending on the approach employed [9][10][11][12].…”