Short-term Solar Irradiance Research Articles

Machine learning and deep learning models based grid search cross validation for short-term solar irradiance forecasting

In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results.

IoT and machine learning models for multivariate very short‐term time series solar power forecasting

AbstractIn solar energy generation, the inherent variability caused by cloud cover and weather events often leads to sudden fluctuations in power outputs. Addressing this challenge, the authors’ study focuses on very short‐term solar irradiance (SI) prediction. Leveraging multivariate time series datasets, the authors improve very short‐term SI predictions. To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. Additionally, the authors’ work pioneers the integration of the Internet of Things (IoT) into solar power systems, a novel approach in the field. The authors leverage LoRa (long range) technology for low‐cost, low‐power, and long‐range wireless control networks. The authors’ study focuses on SI forecasting using long short‐term memory and bi‐directional long short‐term memory (Bi‐LSTM) models, achieving high accuracy. The SI forecasts are evaluated in terms of root‐mean‐square error (RMSE) and mean absolute error in relation to meteorological data and sky image data. The improvement in performance can be attributed to the Bi‐LSTM's bidirectional nature, allowing it to incorporate future information during training, thereby enhancing its predictive capabilities. Overall, the results suggest that the Bi‐LSTM model is more suitable for accurately forecasting SI, particularly in scenarios requiring short‐term predictions based on rapidly changing environmental factors.

Short-term solar irradiance forecasting under data transmission constraints

We report a data-parsimonious machine learning model for short-term forecasting of solar irradiance. The model follows the convolutional neural network – long-short term memory architecture. Its inputs include sky camera images that are reduced to scalar features to meet data transmission constraints. The model focuses on predicting the deviation of irradiance from the persistence of cloudiness (POC) model. Inspired by control theory, a noise signal input is used to capture the presence of unknown and/or unmeasured input variables and is shown to improve model predictions, often considerably. Five years of data from the NREL Solar Radiation Research Laboratory were used to create three rolling train-validate sets and determine the best representations for time, the optimal span of input measurements, and the most impactful model input data (features). For the chosen validation data, the model achieves a mean absolute error of 74.29 W/m2over a time horizon of up to two hours, compared to a baseline 134.35 W/m2 using the POC model.

Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization

The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine-tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.

A structural depth network embedding stacking model based on Moran’s index and seasonal trend for short-term solar irradiance prediction

Solar irradiance prediction of multi-station is influenced by complex spatial characteristics and temporal fluctuations, so how to obtain accurate short-term irradiance prediction by using photovoltaic cluster data becomes a challenge. In order to address the challenge, a structural depth network embedding stacking model based on Moran’s I and seasonal trend for short-term solar irradiance prediction is proposed. Firstly, the deep spatial characteristics of photovoltaic clusters are analyzed by using bivariate Moran index to capture strong correlations between stations. Secondly, seasonal trend decomposition is used to smooth the seasonal factors in time series, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is used to decompose the residual components to reduce the fluctuation of signals and the complexity of data feature mapping. Finally, based on the Stacking framework, Long Short-term Memory Network, Least Absolute Selection and Shrinkage Operator, Gradient Boosted Decision Tree, and Artificial Neural Network are superimposed. And the aggregated sub-modal components are introduced into the robust integration model to predict the solar irradiance in multiple steps. Through the case study of the National Renewable Energy Laboratory open-source dataset in the United States, the conclusions are as follows: (1) The spatio-temporal method in this paper can effectively extract the deep spatial characteristics of photovoltaic stations. (2) The fusion strategy of embedded structured deep network effectively improves the stacking effect. (3) Comparing with many classical forecasting models, it shows that this method can effectively improve the forecasting effect.

Solar Irradiance Forecasting with Natural Language Processing of Cloud Observations and Interpretation of Results with Modified Shapley Additive Explanations

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth’s surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model’s output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry.

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.

Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting

Industrial activities are transitioning towards decarbonization, focusing on renewable energy sources, particularly photovoltaic solar energy. However, the inherent high variability of photovoltaic energy poses challenges. Some of them can be partially addressed by predicting electricity production, which in the case of photovoltaic solar energy is heavily based on solar irradiance prediction. Although extensive research has been conducted in this field, there is a noticeable gap in research regarding very short-term (intra-minute) forecasting under high-variability scenarios. In this proposal, real data from a photovoltaic solar plant in Alderville (Canada) were used to predict irradiance with a horizon of 15 and 30 s. The objective is to make this prediction in near-real time. To achieve this, we propose the use of machine learning algorithms based on decision tree ensembles, due to their low computational training cost and known effectiveness. On the other hand, we propose pre-processing the data through a temporal and spatial correlation analysis between measurements from different sensors. Feature selection analysis allows us to determine the direction of the wind and consequently identify the most relevant panels for model training. This preprocessing enhances the model retraining without the need for external information such as sky images or wind speed and direction on days with highly variable cloud cover. The presented methodology offers promising results with significantly reduced training times, demonstrating the suitability of this semi-online training approach for highly variable time series forecasting.

Short-term global horizontal irradiance forecasting using weather classified categorical boosting

Accurate short-term solar irradiance (SI) forecasting is crucial for renewable energy integration to ensure unit commitment and economic load dispatch. However, hourly SI prediction is very challenging due to atmospheric conditions and weather fluctuations. This study proposes a hybrid approach using weather classification and boosting algorithms for short-term global horizontal irradiance (GHI) forecasting. In data pre-processing steps, we employ random forest for feature selection and K-means clustering for weather classification. The weather-based clustered data is used for the model training using categorical boosting (CatBoost). The proposed weather-classified categorical boosting (WC-CB) scheme is compared with benchmarks in literature like adaptive boosting (AdaBoost), bi-directional long short-term memory (BiLSTM) and gated recurrent unit (GRU) using datasets from two distinct geographical locations obtained from the National Solar Radiation Database (NSRDB). The results show that the proposed WC-CB hybrid approach has lower forecast errors compared to conventional CatBoost modelling. The error reduction of 16% and 39% in root mean square error and 6% and 9% in mean absolute error is recorded for the two datasets, respectively. These findings demonstrate the importance of weather classification in improving forecasting accuracy with potential implications for broader renewable energy applications.

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.

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.

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.

A Short-term solar irradiance forecasting modelling approach based on three decomposition algorithms and Adaptive Neuro-Fuzzy Inference System

In this study, a case study of four Indian cities i.e. Ajmer, Jaipur, Jodhpur and Kota in the state of Rajasthan are considered wherein 30 min ahead data have been obtained via the data site of the National Institute of Wind Energy and Wind Resource (NIWE) on which a proposed Global Horizontal Irradiance (GHI) prediction technique for all seasons is applied. Here, data has been pre-processed using three different signal decomposition algorithms in parallel i.e. Empirical Mode Decomposition (EMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Ensemble Empirical Mode Decomposition (EEMD). Further, based on Pearson's Correlation Coefficient (PCC), corresponding IMFs & corresponding Residual obtained using the three decomposition algorithms are compared amongst each other respectively and that corresponding IMFs & corresponding Residual are chosen for signal reconstruction which are having highest correlation coefficient values. Thereafter, these selected IMFs and Residual from each algorithm are combined to form single input. In this way, three inputs formed from three decomposition algorithms based on the PCC values are fed to Adaptive Neuro-Fuzzy Inference System (ANFIS) for solar irradiance forecasting. The proposed technique shows significantly higher accurate results with less than 2% MAPE for different seasons considered at the site locations considered. Further, it terms of performance, the proposed technique is found to be independent of site location.

Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting

As the proportion of solar power generation increases, accurate solar irradiance forecast used to connect solar power to the grid has become crucial. Multi-parameter prediction is one of the most commonly-used methods for solar irradiance forecast. Effective additional variables can improve the accuracy of the model, while invalid additional variables can lead to over fitting or under fitting of the model. To address this issue, this paper proposes the information gain factor as the basis for proactively selecting input variables. Firstly, the experiment combines 10 kinds of additional variables with GHI and inputs them into five models: auto regressive model (AR), gradient boosting decision tree (GBDT), convolutional neural network (CNN), long short-term memory (LSTM) and convolutional long short-term memory (ConvLSTM). Then, the impact of additional variables on prediction accuracy is analyzed and used as a basis for verifying the feasibility of the proposed method. Finally, the Pearson correlations and information gain factors between these variables and GHI are calculated separately. The results indicate that the information gain factor is more suitable as a basis for selecting input variables than the Pearson coefficient.

Analysing the geographical impact on short-term solar irradiance forecasting using univariate and multivariate time series models

ABSTRACT Sustainable development is significantly hampered by air pollution brought on by greenhouse gas emissions. One of the primary contributors to air pollution is coal, which is used in thermal power plants. Substituting power plants from combustible sources to renewable energy sources can reduce air pollution to an extent. Solar energy is one of the most readily accessible renewable energy sources. High Global Horizontal Irradiance (GHI) is advantageous for deploying solar power plants, allowing maximum electricity production. However, the spatiotemporal factors affecting GHI are not analyzed, which can improve the forecasting accuracy. The proposed work analyzes the geographical impact of solar irradiation using statistical time-series models called ARIMA (Auto Regression Integrated Moving Average) and VAR (Vector Auto Regression) using four datasets from two different locations in India. The statistical time-series models can analyze the impact of the features and can show how the effect improves the performance of the multivariate model compared to the univariate model. The study aims to find the influence of the geographical factors of Global Horizontal Irradiance (GHI) based on the location chosen to build a solar power plant. The effectiveness of the univariate and multivariate models in forecasting solar irradiance is also discussed. The RMSE (root mean square error) analysis shows that the multivariate model VAR outperforms the univariate model in predicting solar irradiance. There is a 1–25% variance in the RMSE values between the ARIMA and VAR models at different locations and a 1–19% variance in MAE (Mean Absolute Error) value, while a 94–98% variance in MAPE (mean absolute percentage error) value. A deep analysis of factors that affect the precision of global horizontal irradiance (GHI) and the causal impact of variables is also analyzed using different variable impact tests. This variable selection is achieved by a feature selection algorithm called Maximum Relevance Minimum Redundancy (MRMR).

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

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.

Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach

Cloud dynamics are the main factor influencing the intermittent variability of short-term solar irradiance, and therefore affect the solar farm output. Sky images have been widely used for short-term solar irradiance prediction with encouraging results due to the spatial information they contain. At present, there is little discussion on the most promising deep learning methods to integrate images with quantitative measures of solar irradiation. To address this gap, we optimise the current mainstream framework using gate architecture and propose a new transformer-based framework in an attempt to achieve better prediction results. It was found that compared to the classical CNN model based on late feature-level fusion, the transformer framework model based on early feature-level prediction improves the balanced accuracy of ramp events by 9.43% and 3.91% on the 2-min and 6-min scales, respectively. However, based on the results, it can be concluded that for the single picture-digital bimodal model, the spatial information validity of a single picture is difficult to achieve beyond 10 min. This work has the potential to contribute to the interpretability and iterability of deep learning models based on sky images.

A New Hybrid Short Term Solar Irradiation Forecasting Method Based on CEEMDAN Decomposition Approach and BiLSTM Deep Learning Network with Grid Search Algorithm

An accurate and efficient forecasting of solar energy is necessary for managing the electricity generation and distribution in today’s electricity supply system. However, due to its random character in its time series, accurate forecasting of solar irradiation is a difficult task; but it is important for grid management, scheduling and its balancing. To fully utilize the solar energy in order to balance the generation and consumption, this paper proposed an ensemble approach using CEEMDAN-BiLSTM combination to forecast short term solar irradiation. In this, Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) extract the inherent characteristics of time series data by decomposing it into low and high frequency Intrinsic Mode Functions (IMF’s) and Bidirectional Long Short Term Memory (BiLSTM) used as a forecasting tool to forecast the solar Global Horizontal Irradiance (GHI). Furthermore, using extensive experimental analysis, the research minimizes the number of IMF’s by integrating the CEEMDAN decomposed component (IMF1–IMF14) in order to increase the prediction accuracy. Then, for each IMF subseries, the trained standalone BiLSTM network are assigned to carry out the forecasting. In last stage, the forecasted results of each BiLSTM network are aggregate to compile final results. Two year data (2012–13) of Delhi, India from National Solar Radiation Database (NSRDB) has been used for training while one year data (2014) used for testing purpose for the same location. The proposed model performance is measured in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Correlation coefficient (R22) and forecast skill (FS). For the comparative analysis of proposed model, several others models: persistence model, unidirectional deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two CEEMDAN based BiLSTM models are developed. The proposed model achieved lowest annual average RMSE (18.86 W/m22, 22.24 W/m22, 26.25 W/m22) and MAPE (2.19%, 4.81%, 6.77%) among the other developed models for 1-hr, 2-hr and 3-hr ahead solar GHI forecasting respectively. The maximum correlation coefficient (R22) obtained by the proposed model is 96.4 for 1-hr ahead respectively; on the other hand, forecast skill (%) of 89% with reference to benchmark model. Various test such as: Diebold Mariano Hypothesis test (DMH) and directional change in forecasting (DC) are used to analyze the sensitivity with reference to the difference in forecasted and observed value.

Short-term solar irradiance forecasting in streaming with deep learning

Solar energy is one of the most common and promising sources of renewable energy. In photovoltaic (PV) systems, operators can benefit from future solar irradiance predictions for efficient load balancing and grid stability. Therefore, short-term solar irradiance forecasting plays a crucial role in the transition to renewable energy. Modern PV grids collect large volumes of data that provide valuable information for forecasting models. Although the nature of these data presents an ideal setting for online learning methodologies, research to date has mainly focused on offline approaches. Hence, this work proposes a novel data streaming method for real-time solar irradiance forecasting on days with variable weather conditions and cloud coverage. Our method operates under an asynchronous dual-pipeline framework using deep learning models. For the experimental study, two datasets from a Canadian PV solar plant have been simulated as streams at different data frequencies. The experiments involve an exhaustive parameter grid search to evaluate four state-of-the-art deep learning architectures: multilayer perceptron (MLP), long-short term memory network (LSTM), convolutional network (CNN), and Transformer network. The obtained results demonstrate the suitability of deep learning models for this problem. In particular, MLP and CNN achieved the best accuracy, with a high capacity to adapt to the evolving data stream.

ACGL-TR: A deep learning model for spatio-temporal short-term irradiance forecast

With the vigorous development of renewable energy, the installed capacity of photovoltaic (PV) power plants continuously expands. For distributed PV systems, spatio-temporal short-term solar irradiance forecasting can give forecasts for multiple sites simultaneously and help improve the generation of the PV power. However, it is difficult to explore appropriate spatio-temporal relationship for high-dimensional and long-historical data. Thus, an Attention Graph Convolution Long short term memory neural network with Tensor Regression (AGCL-TR) is proposed. Firstly, the adaptive adjacent matrices, which represents the relationship among PV sites, are obtained directly from the encoded feature of the history meteorological data of multiple PV sites by self-attention mechanism. Secondly, graph feature along with original data are handled by AGCL to obtain spatio-temporal feature. Thirdly, a TR network is used to decode the higher-order spatio-temporal feature into next step irradiance forecasts for each PV site. The proposed method is validated on an open access dataset, the National Solar Radiation Database (NSRDB), with eight sites randomly selected in Nevada, North America. The experiment demonstrates that the proposed method outperforms the existing spatio-temporal prediction methods in each site, with an improvement of RMSE of 11.9%–19.8%. The proposed method can effectively extract and preserve the spatio-temporal features of high-dimensional data, which can improve the prediction performance in distributed PV systems.