Solar Irradiance Forecasting Research Articles

All sky imaging-based short-term solar irradiance forecasting with Long Short-Term Memory networks

The intermittent nature of solar irradiance, primarily due to cloud movements, leads to rapid short-term fluctuations in the power output of photovoltaic (PV) systems. These fluctuations pose a significant challenge for integrating this renewable energy source into the power grid. Accurate forecasting of solar irradiance is not only crucial but also multi-beneficial. It enables more precise grid management by allowing operators to anticipate power output fluctuations and adjust energy distribution and storage strategies accordingly. This proactive approach reduces the reliance on backup power sources, which are often less sustainable and more expensive. Furthermore, accurate forecasts enhance the overall efficiency and reliability of energy systems by minimizing the impact of power variability on the grid, thereby supporting a more stable and sustainable energy supply.Addressing this need, our study focuses on the development of a forecasting model through innovative feature engineering, systematic design of specific attributes, and optimization of sequence length. The model is tailored to perform efficiently across various weather conditions and offers predictions for a time horizon of 0 to 20 min ahead. Utilizing a Long Short-Term Memory (LSTM) model, we achieve a remarkable ramp Forecast Skill Score of 39% in sunny and 25% in partially cloudy conditions. This work not only contributes to the existing literature but also presents a pioneering methodology for solar energy integration, highlighting the importance and application of accurate short-term solar irradiance forecasting.

Advanced simulation-based predictive modelling for solar irradiance sensor farms

ABSTRACT The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.

Evaluating the cloud effect on solar irradiation by three-dimensional cloud information

A detailed analysis of the effect of clouds on irradiance yields insights into the interaction between clouds and radiation and the precise forecasting of short-term solar radiation. Existing research has mainly focused on the impact of the cloud fraction (CF), i.e., the area distribution of clouds, on radiation. However, three-dimensional cloud information, such as the cloud thickness, also significantly affects solar radiation. This study proposes an innovative index based on the luminance of clouds in all-sky images (ASIs) to characterize the three-dimensional information of clouds, namely, CT, which is usually omitted in the binarization of ASIs. By using two-year synchronous ground irradiance and ASI observation data, the relationship between CT and the cloud radiative effect (CRE) is studied for different types of clouds under two situations, the sun being obscured and unobscured by clouds. The comparison with the commonly used CF indicates that CT has the same or higher negative correlation with the CRE of global horizontal irradiance. Moreover, the correlation between CT and CF with the CREs of direct normal irradiance (DNI) and diffuse irradiance (DFI) is explored in detail. The results indicate that CT has a more negative correlation with the CRE of DNI than that of CF when the sun is obscured, particularly for altocumulus clouds. A detailed discussion indicates that cloud radiative enhancement effects mainly occur under cumulus clouds with a minimum distance between the clouds and the sun (CSMD) of less than three solar radii, but cirrus clouds and altocumulus clouds can also induce cloud radiative enhancement effects.

Binning Based Data Driven Machine Learning Models for Solar Radiation Forecasting in India

Binning Based Data Driven Machine Learning Models for Solar Radiation Forecasting in India

Implementation of AI for forecasting of wind speed and solar irradiation using different Machine Learning techniques

High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and wind systems, they depend on the weather, as compared to the conventional generation. As we know, the balance in managing load and generated power in energy system is very import ant. If the power which is supplied from solar and wind perfectly predictable, the extra cost of operating power system with a large penetration of renewable energy will be reduced. Since, the accurate and reliable forecasting system for renewable sources represents an important topic as a major contribution for increasing non-programmable renewable on over the world. The target of this research is to describe the advanced hybrid evolutionary techniques of computational intelligence applied for PV as well as wind power forecast. The evaluation of this investigation is obtained by comparing different definitions of the forecasting error. Moreover, the meaning of NWP (numerical weather prediction) values based on meteorological information on solar and wind power forecasting at Indore has been highlighted in this research.

Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNet

Solar energy has emerged as a key industry in the field of renewable energy due to its universality, harmlessness, and sustainability. Accurate prediction of solar radiation is crucial for optimizing the economic benefits of photovoltaic power plants. In this paper, we propose a novel spatiotemporal attention mechanism model based on an encoder-translator-decoder architecture. Our model is built upon a temporal AttUNet network and incorporates an auxiliary attention branch to enhance the extraction of spatiotemporal correlation information from input images. And utilize the powerful ability of edge intelligence to process meteorological data and solar radiation parameters in real-time, adjust the prediction model in real-time, thereby improving the real-time performance of prediction. The dataset utilized in this study is sourced from the total surface solar incident radiation (SSI) product provided by the geostationary meteorological satellite FY4A. After experiments, the SSIM has been improved to 0.86. Compared with other existing models, our model has obvious advantages and has great prospects for short-term prediction of surface solar incident radiation.

A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model

Prediction of solar irradiance is crucial for minimizing energy costs and ensuring high power quality in electrical power grids that incorporate distributed solar photovoltaic generation. Traditional methods have focused mainly on time series prediction of irradiance or studies of the relationship between irradiance and environmental factors at a given moment. This paper presents a novel global horizontal irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model. In the proposed method, cloud fraction, cloud albedo and aerosol optical thickness are selected as critical characteristic factors in the sunlight-atmosphere interaction. Long short-term memory (LSTM) is utilized to extract temporal features of the critical characteristic factors in various physical processes of atmospheric optics and predict their values. Subsequently, back propagation (BP) neural network is constructed to investigate the relationship between these characteristic factors and global horizontal irradiance under a given point in time. The global horizontal irradiance is predicted by combining the predicted values of critical characteristic factors and BP neural network for modelling irradiance decay process. To demonstrate the superior performance of the proposed model, the results obtained by the proposed method are compared with those of the LSTM network model for time series forecasting, in which six error evaluation indicators are used in the comparison. The proposed method for solar irradiance forecasting is verified in different time scales and under various weather conditions and shows better accuracy comparing with time series forecasting methods.

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

Solar radiation forecasting using gradient boosting based ensemble learning model for various climatic zones

India is seeing a massive boost in solar power installations in the recent years. It is expected that solar energy utilization will be increasing exponentially in near future. Solar radiation forecasting is an essential tool to obtain the optimal performance out of the solar systems. An ensemble model using gradient boosting has been developed for hourly global horizontal irradiance forecasting is proposed for the various climatic zones of India. The gradient boost-based model is benchmarked with Auto Regressive Integrated Moving Average (ARIMA),2-layer feed forward neural network, and Long Short-Term Memory (LSTM). Gradient boosting obtained with marginally better performance metrics, mean absolute error (20.97 W/m2) and mean square error (1357 W2/m4) as compared to that of the neural network (21.66 W/m2, 1479.53 W2/m4) and performed much better than ARIMA and LSTM in terms of mean absolute error (251.66 W/m2) & mean square error (82,290 W2/m4) and mean absolute error (67.60 W/m2) & mean square error (8123 W2/m4). The diverse climatic zones in India and feature importance analysis have been considered during the model development and testing phase. The proposed model is also intended to have practical implications in obtaining solar irradiance data in locations with minimal availability of solar radiation data and solar power systems due to the model ability to provide good performance metrics in a smaller timeframe making it an ideal candidate for real-time solar power predicting model.

An innovative machine learning based on feed-forward artificial neural network and equilibrium optimization for predicting solar irradiance

As is known, having a reliable analysis of energy sources is an important task toward sustainable development. Solar energy is one of the most advantageous types of renewable energy. Compared to fossil fuels, it is cleaner, freely available, and can be directly exploited for electricity. Therefore, this study is concerned with suggesting novel hybrid models for improving the forecast of Solar Irradiance (IS). First, a predictive model, namely Feed-Forward Artificial Neural Network (FFANN) forms the non-linear contribution between the IS and dominant meteorological and temporal parameters (including humidity, temperature, pressure, cloud coverage, speed and direction of wind, month, day, and hour). Then, this framework is optimized using several metaheuristic algorithms to create hybrid models for predicting the IS. According to the accuracy assessments, metaheuristic algorithms attained satisfying training for the FFANN by using 80% of the data. Moreover, applying the trained models to the remaining 20% proved their high proficiency in forecasting the IS in unseen environmental circumstances. A comparison among the optimizers revealed that Equilibrium Optimization (EO) could achieve a higher accuracy than Wind-Driven Optimization (WDO), Optics Inspired Optimization (OIO), and Social Spider Algorithm (SOSA). In another phase of this study, Principal Component Analysis (PCA) is applied to identify the most contributive meteorological and temporal factors. The PCA results can be used to optimize the problem dimension, as well as to suggest effective real-world measures for improving solar energy production. Lastly, the EO-based solution is yielded in the form of an explicit formula for a more convenient estimation of the IS.

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model’s performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m2.

Short-Term Solar Irradiance Prediction with a Hybrid Ensemble Model Using EUMETSAT Satellite Images

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy-driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing the prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 h ahead. The incorporation of EUMETSAT satellite image pixel data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes a detailed analysis of the derivation of the GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional satellite (SAT) and numerical weather prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to a smart persistent model and consistently highlight the superiority of the hybrid ensemble model for a short-term prediction window of 1 to 6 h. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid.

An interpretable horizontal federated deep learning approach to improve short-term solar irradiance forecasting

Solar irradiance forecasting is critical in the planning and operation of solar power plants for production scheduling, energy trading, and maintenance planning. Accurate solar irradiance forecasting is crucial for efficient and stable power systems. Various deep learning (DL) methods have been researched in solar irradiance forecasting because of their powerful capabilities of nonlinear mapping. However, traditional DL models face challenges with small sample sizes, irradiance fluctuations, and data privacy concerns. On this foundation, it is also critical to ensure the interpretability of artificial intelligence (AI) models, as interpretable and trustworthy predictive models can help operators make effective decisions. Focusing on the above-mentioned issues, a privacy-preserving, interpretable, and DL-based model is proposed to improve the forecast accuracy of solar irradiance while ensuring the data available but not visible to each participant and prediction comprehensibility. This model is constructed by horizontal federated framework (HFF), self-attention (SA) mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) network, named HFF-based SA-CNN-LSTM. A series of comprehensive experiments spanning various climate regions are designed to assess model performance under different federated conditions. The experiments show that this method significantly improves forecasting accuracy, especially in scenarios with incomplete data and highly fluctuating irradiance when federated with other datasets, and the model exhibits interpretability, adjusting attention for precise predictions at different time points. Moreover, the effects of different federated scales on the predictive performance of the target model are fully discussed for future practical applications. These findings hold significant promise for enhancing the accuracy of irradiance forecasting, contributing to energy efficiency, grid stability, and data privacy.

Solar Irradiance Forecasting using Improved Sample Convolution and Interactive learning

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts.

Advancing solar energy forecasting with modified ANN and light GBM learning algorithms

<abstract> <p>In the evolving field of solar energy, precise forecasting of Solar Irradiance (SI) stands as a pivotal challenge for the optimization of photovoltaic (PV) systems. Addressing the inadequacies in current forecasting techniques, we introduced advanced machine learning models, namely the Rectified Linear Unit Activation with Adaptive Moment Estimation Neural Network (RELAD-ANN) and the Linear Support Vector Machine with Individual Parameter Features (LSIPF). These models broke new ground by striking an unprecedented balance between computational efficiency and predictive accuracy, specifically engineered to overcome common pitfalls such as overfitting and data inconsistency. The RELAD-ANN model, with its multi-layer architecture, sets a new standard in detecting the nuanced dynamics between SI and meteorological variables. By integrating sophisticated regression methods like Support Vector Regression (SVR) and Lightweight Gradient Boosting Machines (Light GBM), our results illuminated the intricate relationship between SI and its influencing factors, marking a novel contribution to the domain of solar energy forecasting. With an R<sup>2</sup> of 0.935, MAE of 8.20, and MAPE of 3.48%, the model outshone other models, signifying its potential for accurate and reliable SI forecasting, when compared with existing models like Multi-Layer Perceptron, Long Short-Term Memory (LSTM), Multilayer-LSTM, Gated Recurrent Unit, and 1-dimensional Convolutional Neural Network, while the LSIPF model showed limitations in its predictive ability. Light GBM emerged as a robust approach in evaluating environmental influences on SI, outperforming the SVR model. Our findings contributed significantly to the optimization of solar energy systems and could be applied globally, offering a promising direction for renewable energy management and real-time forecasting.</p> </abstract>

Very short-term probabilistic and scenario-based forecasting of solar irradiance using Markov-chain mixture distribution modeling

This study investigates probabilistic and scenario-based forecasting of solar irradiance with Markov-chain mixture (MCM) distribution modeling, Persistence Ensemble (PeEn) and Climatology. Forecasts from MCM models with uniform and empirical emission distribution settings, respectively, are compared with PeEn and Climatology in terms of probabilistic forecasting performance. The MCM model is also extended with scenario generation capabilities and compared to scenario generation of the Climatology by means of Monte Carlo sampling. Forecasts were made on minute resolution normalized solar irradiance, i.e. the clear-sky index, from National Renewable Energy Laboratory and Swedish Meteorological and Hydrological Institute for two climatic regions: Oahu, Hawaii, USA and Norrköping, Sweden, respectively. Results show that the MCM models are neither necessarily the most reliable, nor the sharpest in terms of Prediction Normalized Average Width (PINAW), but they are the most accurate in terms of Continuous Ranked Probability Score (CRPS). MCM models with uniform and empirical emission distribution settings perform similar in the tested probabilistic forecasting metrics. In terms of scenario forecasting, MCM models with N=30 perform similar in probability distribution goodness-of-fit and autocorrelation Mean Absolute Error (MAE) and superior to N=2 and N=10 number of states. Mathematically, forecasts from the MCM model with empirical distribution setting are shown to correspond to PeEn and Climatology forecasts given special settings of the MCM model. Based on the conclusions, the suggestion is to use the MCM scenario forecast generator with uniform emission distribution setting as benchmark for scenario forecasts of very short-term solar irradiance.

Holistic and Lightweight Approach for Solar Irradiance Forecasting

Holistic and Lightweight Approach for Solar Irradiance Forecasting

The Application of Deep Learning Methods in the Forecasting and Imputation of Solar Irradiance Data

Accurate solar radiation forecasts are necessary for solar power plants and local energy grids. Precise solar predictions can optimise plant operations and assist in managing power generation fluctuations. High-quality solar data are required to perform solar forecasting and may be accessed through open databases such as the Baseline Surface Radiation Network. Due to maintenance and malfunctioning equipment, these databases may have missing data. Deep learning methods can extract relevant features and non-linearities in data, which make them suitable for forecasting and imputation tasks. The development of the transformer architecture in 2017 marked an improvement from recurrent sequence processing using RNN (recurrent neural net) architectures to parallel sequence handling using self-attention mechanisms. Previous deep learning irradiance forecasting models utilised a RNN or RNN extension architecture. This paper presents novel applications of Transformer models to solar imputation and forecasting. Two deep learning architectures – Transformer and SAITS (Self-Attention-based Imputation for Time Series) – were trained to impute time series of global horizontal irradiance. Additionally, a Temporal Fusion Transformer (TFT) deep learning architecture was trained to forecast future values of global horizontal irradiance time series. Feature concatenation allows for the combination of differing exogenous variables into a single input vector for a deep learning solar forecasting model. Previous solar forecasting models do not incorporate cloud data or otherwise utilise an exogenous simple categorical mask to indicate cloud cover. In this paper, GOES-16 cloud optical depth and particle size datasets provide an improved representation of cloud properties. Classical imputation and forecasting models were also developed and used as baselines for comparison with deep learning models. The models were evaluated using standard statistical metrics for time series. It was found that the deep learning TFT forecasting model produced better forecasts than the classical regression model. However, the classical KNN imputer performed better than the two deep learning imputation models.

Evaluation and performance comparison of different models for global solar radiation forecasting: a case study on five cities

AbstractRecently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ($$R^{2}$$ R 2 ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ r , are good, and their values range from 0.7863 to 1.9097 (MJ m−2 day−1), from 0.6430 to 1.7060 (MJ m−2 day−1), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes.