Short-term Solar Irradiance Research Articles

Next-Month Prediction of Hourly Solar Irradiance based on Long Short-Term Memory Network

Today, in parallel with the population growth and the advancement of technology, development concerns have started to arise in terms of country administrators. Therefore, alternative solutions to classical energy sources are sought. Renewable energy sources are one of the preferred energy sources today. The popularity of renewable energy sources, including solar energy, is increasing day by day. Solar energy has the potential and accessibility to spread faster than other renewable energy sources. Since Türkiye is located in a region with a high potential in terms of solar energy, which is generally called the sun belt, it is a right decision to prefer solar energy as an energy source in our region. In this study, time series prediction using Long Short-Term Memory (LSTM) Network method is used for short-term solar irradiance estimation. In order to demonstrate the success of the results, a comparison was made with the Artificial Neural Network (ANN) method. Finally, prediction results of solar irradiance were compared with statistical tests and error analyzes were given in numerically.

A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

A hybrid multi-modal ensemble learning model is proposed for short-term solar irradiance forecasting based on historical observations and sky images in this paper. In the proposed model, historical observations of solar irradiance are utilized to extract temporal characteristics, and ground-based sky images are used as an exogenous input to offer cloud cover information. A powerful ensemble learning model, Extreme Gradient Boosting (XGBoost), is employed to capture the function relationships between input features and future observations. Since mean squared error loss is sensitive to the extreme large or small historical irradiance, a novel loss function is proposed to improve robustness of XGBoost. In order to find out the best controlling parameters, Rao-1 algorithm is employed for its easy operation. To validate performance of the proposed method, a solar irradiance dataset containing three-year historical observations and ground-based sky images collected from Folsom is employed. Meanwhile, five commonly applied methods, LASSO, Ridge regression, support vector regression, boosted regression trees, the generic XGBoost, are considered as benchmarking methods. The forecasting horizons from 5 to 30 min are considered for all compared methods while two metrics, mean absolute error and root mean squared error, are computed. Experimental results prove that the proposed hybrid model has better forecasting performance compared with benchmarking methods over all forecasting horizons.

Deep Reinforcement Learning-Based Self-Scheduling Strategy for a CAES-PV System Using Accurate Sky Images-Based Forecasting

Compressed air energy storage (CAES) is a scalable and clean energy storage technology with great potential in renewables accommodation. From the point of view of the facility owner participating in the energy market, the profit of a CAES-PV system’s coordinated operation is still at a notable risk. This paper addresses this problem by using a novel model-free deep reinforcement learning (DRL) method to optimize the CAES energy arbitrage in the presence of a sky images-based short-term solar irradiance forecasting model. To overcome the risk associated with the highly intermittent solar power productions, and thus efficient participation in an electricity market, a hybrid forecasting model based on 2-D convolutional neural networks (CNNs) and bidirectional long short-term memory (BLSTM) units is developed to capture high levels of abstractions in solar irradiance data, especially during cloudy days. Moreover, the thermodynamic characteristics of the CAES facility are considered to achieve more realistic real-time scheduling results. The comparative results based on a realistic-based case study demonstrate the effectiveness and applicability of the proposed framework compared to the state-of-the-art methods in the recent literature.

Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method

Accurate short term solar irradiation forecasting is necessary for smart grid stability and to manage bilateral contract negotiations between suppliers and customers. Traditional machine learning methods are unable to acquire and rectify nonlinear characteristics from solar dataset, which not only complicates model construction but also affect prediction accuracy. To address these issues, a deep learning based architecture with predictive analysis strategy is developed in this manuscript. In the first stage, the original solar irradiation sequences are divided into many intrinsic mode functions to generate a prospective feature set using a sophisticated signal decomposition technique. After that, an iteration method is used to generate a prospective range of frequency related to deep learning model. This method is created by linked algorithm using the GA and deep learning network. The findings by the proposed model employing sequences obtained by the preprocessing methodology considerable improve prediction accuracy as comparison to conventional models. In contrast, when confronted with a high resolution dataset derived from big data set, the chosen dataset may not only conduct a huge data reduction, but also enhances forecasting accuracy up to 22.74 percent over a variety of evaluation metrics. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset.

Development of a Short-term Solar Irradiance Forecasting using Satellite Image in Combination with Numerical Weather Prediction Model

Development of a Short-term Solar Irradiance Forecasting using Satellite Image in Combination with Numerical Weather Prediction Model

Promoting efficient production of scytonemin in cell culture of Nostoc flagelliforme by periodic short-term solar irradiation

The ultraviolet-screening pigment scytonemin is bio-synthesized in some sheathed cyanobacteria, exhibiting important ecological and medicinal values. Scytonemin is recognized to be predominantly induced by ultraviolet (UV)-A/B, but UV radiation is often inhibitory for cyanobacterial biomass increase. Here, we found that short-term shock (within 1 h) of natural sunlight could trigger a persistent production of scytonemin in cell suspension culture of Nostoc flagelliforme for several days. We thus exposed the cultures to solar radiation with different time intervals and durations, and found that everyday 30-min solar irradiation was the most effective for achieving the scytonemin production with less growth inhibition. Besides, the technological potential could be advanced by supplementing NaHCO3 or tryptophan in the cultural medium. This work presents a good example of rationally utilizing environmental solar radiation for effectively producing UV-inducible biochemicals in cyanobacteria.

Short term solar irradiation forecasting using Deep neural network with decomposition methods and optimized by grid search algorithm

Due to the variable nature of solar energy, it is necessary to manage a bilateral contract negotiation between suppliers and customers. Therefore, to fulfil this condition, this paper proposed an ensemble approach to forecast the solar irradiation. The signal processing techniques Variational Mode Decomposition (VMD) and Discrete Wavelet Transform (DWT) used with deep neural network to forecast the solar irradiation. The hyperparameters of deep learning model are optimized using grid search optimization with in a suitable tolerable search range. The data of three years (2012-14) is used; where data of year 2012-2013 is used to train model and testing is done on data of year 2014 for New Delhi location. Among all developed models, Bi-LSTM-VMD-Grid Search performance is better in terms of RMSE (5.456W/m2), MAPE (0.948%) and R2(0.924%), Because Bi-LSTM process the information twice and faster than other algorithms and VMD refine the quality of input data better as comparison to DWT. The result of proposed model is compared with existing techniques that predicted the solar irradiation and the forecasted results are more efficient and reliable.

Development of Short-term Solar Irradiance Forecasting Model Using ARIMA and Seasonal ARIMA in Daejeon

Development of Short-term Solar Irradiance Forecasting Model Using ARIMA and Seasonal ARIMA in Daejeon

Short-Term Solar Irradiance Forecasting Using CNN-1D, LSTM, and CNN-LSTM Deep Neural Networks: A Case Study With the Folsom (USA) Dataset

Abstract In this paper, solar irradiance short-term forecasts were performed considering time horizons ranging from 5 min to 30 min, under a 5 min time-step. Global horizontal irradiance (GHI) and direct normal irradiance (DNI) were computed using deep neural networks with 1-dimensional convolutional neural network (CNN-1D), long short-term memory (LSTM), and CNN-LSTM layers on the benchmarking dataset FOLSOM, which is formed by predictors obtained by recursive functions on the clear sky index time series and statistical attributes extracted from images collected by a camera pointed to the zenith, characterizing endogenous and exogenous variables, respectively. To analyze the endogenous predictors influence on the accuracy of the networks, the performance was evaluated for the cases with and without them. This analysis is motivated, to our best knowledge, by the lack of works that cite the FOLSOM dataset using deep learning models, and it is necessary to verify the impact of the endogenous and exogenous predictors in the forecasts results for this specific approach. The accuracy of the networks was evaluated by the metrics mean absolute error (MAE), mean bias error (MBE), root-mean-squared error (RMSE), relative root mean squared error (rRMSE), determination coefficient (R2), and forecast skill (s). The network architectures using isolated CNN-1D and LSTM layers generally performed better. The best accuracy was obtained by the CNN-1D network for a horizon of 10 min ahead reaching an RMSE of 36.24 W/m2, improving 11.15% on this error metric compared to the persistence model.

Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data.

Concerns over fossil fuels and depletable energy sources have motivated renewable energy sources utilization, such as solar photovoltaic (PV) power. Utilities have started penetrating the existing primary grid with renewable energy sources. However, penetrating the grid with photovoltaic energy sources degrades the stability of the whole system because photovoltaic power depends on solar irradiance, which is highly intermittent. This paper proposes a prediction method for non-stationary solar irradiance. The proposed method uses an adaptive extreme learning machine. The extreme learning machine method uses approximated sigmoid and hyper-tangent functions to ensure faster computational time and more straightforward microcontroller implementation. The proposed method is analyzed using the hourly weather data from a specific site at Najran University. The data are preprocessed, trained, tested, and validated. Several evaluation metrics, such as the root mean square error, mean square error, and mean absolute error, are used to evaluate and compare the proposed method with other recently introduced approaches. The results show that the proposed method can be used to predict solar irradiance with high accuracy, as the mean square error is 0.1727. The proposed approach is implemented using a solar irradiance sensor made of a PV cell, a temperature sensor, and a low-cost microcontroller.

Performance assessment of SARIMA, MLP and LSTM models for short-term solar irradiance prediction under different climates in Morocco

Photovoltaic (PV) production is highly dependent on global solar irradiance (GHI) and often experiences irregular fluctuations. With the continuous increase in PV penetration rates, GHI forecasting methods are becoming important to ensure optimal management of the energy produced. In this study, three forecasting models – Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Seasonal Autoregressive Integrated Moving Average (SARIMA) were evaluated to predict GHI one day ahead. Ground measurement data from four sites in Morocco and weather forecasts from the Global Forecast System (GFS) were used to perform the forecast. Results show that forecasts based on MLP and LSTM are more accurate than SARIMA and persistence even if under complicated weather conditions. For clear days with low variability, the RMSE for LSTM, MLP M1, and MLP M2 are 18.58, 12.97, and 45.20 W/m2. For cloudy days with high variability, the RMSE are 103, 60.80 W/m2, and 89.17 W/m2, respectively.

Short-term solar irradiance prediction based on spatiotemporal graph convolutional recurrent neural network

Solar irradiance data include temporal information and geospatial information, so solar irradiance prediction can be regarded as a spatiotemporal sequence prediction problem. However, at present, most of the research is based on time series prediction models, and the research studies on spatial-temporal series prediction models are relatively few. Therefore, it is necessary to integrate spatial-temporal information to construct a spatial-temporal sequence prediction model for research. In this paper, the spatial-temporal prediction model based on graph convolutional network (GCN) and long short-term memory network (LSTM) was established for short-term solar irradiance prediction. In this model, solar radiation observatories were modeled as undirected graphs, where each node corresponds to an observatory, and a GCN was used to capture spatial correlations between sites. For each node, temporal features were extracted by using a LSTM. In order to evaluate the prediction performance of this model, six solar radiation observatories located in the Xinjiang region of China were selected; together with widely used persistence model seasonal autoregressive integrated moving average and data-driven prediction models such as convolutional neural network, recurrent neural network, and LSTM, comparisons were made under different seasons and weather conditions. The experimental results show that the average root mean square error of the GCN-LSTM model at the six sites is 62.058 W/m2, which is reduced by 9.8%, 14.3%, 6.9%, and 3.3%, respectively, compared with other models; the average MAE is 25.376 W/m2, which is reduced by 27.7%, 26.5%, 20.1%, and 11%, respectively, compared with other models; the average R2 is 0.943, which is improved by 1.4%, 2.2%, 0.8%, and 0.4%, respectively, compared with other models.

Infinite hidden Markov model for short-term solar irradiance forecasting

Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify.In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler.Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon.For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material.

Combined Long Short-Term Memory Network-Based Short-Term Prediction of Solar Irradiance

Achieving the highly accurate and generic prediction of solar irradiance is arduous because solar irradiance possesses intermittent randomness and is influenced by meteorological parameters. Therefore, this paper endeavors a new combined long short-term memory network (CLSTMN) with various influence meteorological parameters as inputs. We investigated the proposed predictive model applicability for short-term solar irradiance prediction application and validated it in the real-time metrological dataset. The proposed prediction model is combined and accumulated by various inputs, incurring six individual long short-term memory models to improve solar irradiance prediction accuracy and generalization. Thus, the CLSTMN-based solar irradiance prediction can be generic and overcome the metrological parameters concerning variability. The experimental results ensure good prediction accuracy with minimal evaluation metrics of the proposed CLSTMN for solar irradiance prediction. The RMSE, MAPE, and MSE achieved based on the proposed CLSTMN one-hour-ahead prediction are 7.7729 × 10 − 04 , 8.2479 × 10 − 05 , and 6.0419 × 10 − 07 and for six-hour-ahead prediction are 0.0157, 0.0017, and 2.4627 × 10 − 04 for sunny days, and for cloudy days, the RMSE, MAPE, and MSE achieved based on the proposed CLSTMN one-hour-ahead prediction are 1.2969 × 10 − 04 , 1.6882 × 10 − 04 , and 1.6819 × 10 − 08 and for six-hour-ahead prediction are 0.0176, 0.0043, and 3.0863 × 10 − 04 , respectively. Finally, we investigate the CLSTMN performance effectiveness by comparative analysis with well-known baseline models. The investigative study shows the surpassing prediction performance of the proposed CLSTMN for short-term solar irradiance prediction.

Short Term Solar Irradiation Forecasting using CEEMDAN Decomposition Based BiLSTM Model Optimized by Genetic Algorithm Approach

An accurate short-term solar irradiation forecasting is requiredregarding smart grid stability and to conduct bilateral contract negotiations between suppliers and customers. Traditional machine learning models are unable to acquire and to rectify nonlinear properties from solar datasets, which not only complicate model formation but also lower prediction accuracy. The present research paper develops a deep learningbased architecture with a predictive analytic technique to address these difficulties. Using a sophisticated signal decomposition technique, the original solar irradiation sequences are decomposed into multiple intrinsic mode functions to build a prospective feature set. Then, using an iteration strategy, a potential range of frequency associated to the deep learning model is generated. This method is developed utilizing a linked algorithm and a deep learning network. In comparison with conventional models, the suggested model utilizes sequences generated through preprocessing methods, significantly improving prediction accuracywhen confronted with a high resolution dataset created from a big dataset.On the other hand, the chosen dataset not only performs a massive data reduction, but also improves forecasting accuracy by up to 20.74 percent across a range of evaluation measures. The proposed model achieves lowest annual average RMSE (1.45W/m2), MAPE (2.23%) and MAE (1.34W/m2) among the other developed models for 1-hr ahead solar GHI, respectively, whereas forecast-skill obtained by the proposed model is 59% with respect to benchmark model. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset

Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network

Accurate forecasting is indispensable for improving solar renewables integration and minimizing the effects of solar energy's intermittency. Existing research on time series solar forecasting confronts challenges such as determining the accurate hyperparameters and flexibility in considering meteorological parameters. This study proposes a novel deep learning model, namely an optimized stacked Bi-directional Long Short-Term Memory(BiLSTM)/ Long Short-Term Memory(LSTM) model to forecast univariate and multivariate hourly time series data by integrating stacked LSTM layers, drop out architecture, and LSTM based model. The performance of the model is enhanced by Bayesian optimization with the tuning of six relevant hyperparameters. To evaluate the model, standard Global Horizontal Irradiance (GHI) and observed Plane of Array (POA) irradiance with meteorological real-world solar data from Sweihan Photovoltaic Independent Power project in Abu Dhabi, UAE, and NREL solar data for year-round data are forecasted. Furthermore, the performance of the proposed algorithm is also evaluated under weather uncertainty for different climate types. The forecasting accuracy is evaluated based on various performance metrics and it is observed that the proposed model offered the best R2 values, 0.99 for univariate as well as multivariate models using GHI data and 0.97 using POA data. The findings suggest that the proposed model is a reliable technique for solar prediction due to its comparable performance with both GHI and POA in terms of accuracy.

An efficient robust optimized functional link broad learning system for solar irradiance prediction

A new machine learning approach for forecasting short-termsolar irradiance is presented in this paper considering different weather conditions and time horizon in a microgrid environment. Using historical solar irradiance, a short-term forecasting model known as Robust optimized functional link broad learning system (FLBLS) is developed. Here the original input comprising delayed historical solar irradiance samples is initially expanded by exponential trigonometric functions and the expanded features are then used to create a large number of feature nodes using random weights and biases. Further the newly created features are fed as inputs to the enhancement nodes forming a broad learning system (BLS). This newly formed flat FLBLS facilitates incremental learning in fast remodelling of the network if needed for efficient performance without a retraining process unlike deep neural networks using several hidden layers and filters, etc. The proposed approach is designed on the basis of the standard random vector functional link network (RVFLN), however unlike the traditional RVFLN that applies inputs directly and set up the enhancement nodes, in our proposed approach, initially the inputs are expanded and map the expanded inputs to generate a set of mapped features. It reduces the lengthy process unlike deep neural networks that involves the back propagation algorithm for error minimization. The proposed FLBLS approach simplifies the learning process with the proper choice of feature nodes as it can be implemented to a flat network or a simpler network such as ELM that requires only the simple calculation of the output weight and obtain the final output where retraining is not required for adding extra feature nodes or enhancement nodes like deep neural architectures. Also, the minimum variance strategy with the training samples is useful in rejecting the effects of noise and outliers in the system and make the network robust which gets reflected in computing output weights using generalized least square. The minimum variance strategy further enhances the performance of the proposed system. To improve the prediction accuracy, the FLBLS parameters are optimized using an efficient metaheuristic chaotic firefly-sine cosine levy flight algorithm. Also, to reject the effects of noise and outliers and make the network robust a minimum variance strategy is used with the training samples that gets reflected in computing output weights using generalised least squares. The suggested method is experimented rigorously and analysed with different time frames designed for 30 min and 60 min ahead solar irradiance prediction. The obtained results from experiments confirm that the proposed FLBLS approach outperforms other randomized neural network forecasting methods as well many state-of-the-art methods using solar irradiance data of Hyderabad, India collected from National Renewable Energy Laboratory (NREL). This approach exhibits with the lowest errors in different case studies among all the compared methods. This FLBLS designed approach is an efficient approach for the purpose of solar irradiance prediction with highly nonlinear data.

Ten-minute prediction of solar irradiance based on cloud detection and a long short-term memory (LSTM) model

With the inherent volatility of solar radiation, accurate short-term prediction of solar irradiance is essential to cost-efficiently stabilise and operate power grids. Cloud is the major influencing factor for variations in solar irradiance at a minute scale. This study proposes a short-term solar irradiance prediction model based on a deep learning network. Firstly, a new hybrid cloud detection method is proposed to calculate cloud coverage under different sky condition. The relationship between cloud coverage at the current moment and the global horizontal irradiance (GHI) 10 min later is directly analysed to avoid the error of assuming cloud motion as rigid motion. Meteorological parameters with strong correlation with the GHI in ten minutes are admitted into the model input, mainly including relative humidity and Aerosol Optical Depth (AOD) of 500 nm in this study. The GHI of two-time intervals ahead are also selected after autocorrelation analysis to represent other comprehensive impacts. Together with cloud, the meteoritical parameters and the historical GHI are taken as the input of a deep learning network simultaneously, which is based on long short-term memory network (LSTM) and optimised by Bayesian Optimisation (BO). The solar irradiance after ten minutes is the output of the model. Experiments were conducted to evaluate the performance of the proposed forecast model comparing with some benchmark models. Results showed that the proposed model outperformed other models with a normalised root mean square error (nRMSE) of 15.25% under all sky conditions. An improvement of 8.23% was reached compared with persistent model.

Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m2) and the Plane Of Array (POA) irradiance (unit: W/m2) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), and R2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.

Short-Term Solar Irradiance Prediction Based on Multichannel LSTM Neural Networks Using Edge-Based IoT System

Most photovoltaic power generation methods use global level irradiance (GHI) as the main input and output. However, randomness, instability, and intermittency are the main factors that seriously degrade the solar irradiance prediction results. Traditional data-driven prediction models are difficult for accurate predictions. In this study, a multichannel deep learning model named multichannel, wavelet transform combining convolutional neural network and bidirectional long short-term memory (MC-WT-CBiLSTM) framework-based edge computing and IoT system is proposed to improve the GHI prediction accuracy. The solar irradiance data is decomposed by wavelet transform to reduce data complexity. Each decomposed component is inputted into the multichannel MC-CBiLSTM deep learning framework for forecasting and combined to produce the final results. The comparison with existing solar irradiance forecasting methods shows that the proposed MC-WT-CBiLSTM deep learning framework has obvious advantages in the prediction of various time horizons.