Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning

Solar energy will be a great alternative to fossil fuels since it is clean and renewable.The photovoltaic(PV)mechanism produces sunbeams’green energy without noise or pollution.The PV mechanism seems simple,seldom malfunctioning,and easy to install.PV energy productivity significantly contributes to smart grids through many small PV mechanisms.Precise solar radiation(SR)prediction could substantially reduce the impact and cost relating to the advancement of solar energy.In recent times,several SR predictive mechanism was formulated,namely artificial neural network(ANN),autoregressive moving average,and support vector machine(SVM).Therefore,this article develops an optimal Modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction(OMBGRU-SRP)for energy management.The presented OMBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process.To accomplish this,the presented OMBGRU-SRP technique performs data preprocessing to normalize the solar data.Next,the MBGRU model is derived using BGRU with an attention mechanism and skip connections.At last,the hyperparameter tuning of the MBGRU model is carried out using the satin bowerbird optimization(SBO)algorithm to attain maximum prediction with minimum error values.The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild.Many experiments were conducted to demonstrate the enhanced SR prediction performance.The experimental values highlighted the supremacy of the OMBGRU-SRP algorithm over other existing models.

A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model accuracy.Various models were developed and trained with different preprocessing stages.Finally,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction errors.Results indicated that models trained on raw data generally performed better than those on stripped data.The Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling techniques.The study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.

Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Solar energy represents one of themost important renewable energy sources contributing to the energy transition process.Considering that the observation of daily global solar radiation(GSR)is not affordable in some parts of the globe,there is an imperative need to develop alternative ways to predict it.Therefore,the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions,such as the majority of Spanish territory.Here,four ensemble-based hybrid models were developed by hybridizing Additive Regression(AR)with Random Forest(RF),Locally Weighted Linear Regression(LWLR),Random Subspace(RS),and M5P.The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation.The testing phase outcomes demonstrated that the ARRF models outperform all other hybrid models.The provided models were validated by statisticalmetrics,such as the correlation coefficient(R)and root mean square error(RMSE).The results proved that Scenario#6,utilizing extraterrestrial solar radiation,relative humidity,wind speed,and mean,maximum,and minimum ambient air temperatures as the model inputs,leads to the most accurate predictions among all scenarios(R=0.968–0.988 and RMSE=1.274–1.403 MJ/m^(2)・d).Also,Scenario#3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations.The AD-RF model was the best predictive,followed by AD-RS and AD-LWLR.Hence,this study recommends new effective methods to predict GSR in semiarid regions.

Prediction of solar activities:Sunspot numbers and solar magnetic synoptic maps

The evolution of solar magnetic fields is significant for understanding and predicting solar activities.And our knowledge of solar magnetic fields largely depends on the photospheric magnetic field.In this paper,based on the spherical harmonic expansion of the photospheric magnetic field observed by Wilcox Solar Observatory,we analyze the time series of spherical harmonic coefficients and predict Sunspot Number as well as synoptic maps for Solar Cycle 25.We find that solar maximum years have complex short-period disturbances,and the time series of coefficient g_(7)~0 is nearly in-phase with Sunspot Number,which may be related to solar meridional circulation.Utilizing Long Short-Term Memory networks(LSTM),our prediction suggests that the maximum of Solar Cycle 25 is likely to occur in June 2024 with an error of 8 months,the peak sunspot number may be 166.9±22.6,and the next solar minimum may occur around January 2031.By incorporating Empirical Mode Decomposition,we enhance our forecast of synoptic maps truncated to Order 5,validating their relative reliability.This prediction not only addresses a gap in forecasting the global distribution of the solar magnetic field but also holds potential reference value for forthcoming solar observation plans.

Predicting the 25th solar cycle using deep learning methods based on sunspot area data

It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the solar dynamo of simulation and space mission planning. In this paper, we employ the long-shortterm memory(LSTM) and neural network autoregression(NNAR) deep learning methods to predict the upcoming 25 th solar cycle using the sunspot area(SSA) data during the period of May 1874 to December2020. Our results show that the 25 th solar cycle will be 55% stronger than Solar Cycle 24 with a maximum sunspot area of 3115±401 and the cycle reaching its peak in October 2022 by using the LSTM method. It also shows that deep learning algorithms perform better than the other commonly used methods and have high application value.

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the environment.Solar Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation systems.Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time.In this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and precipitation.Therefore,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting data.In order to perform long-term collection of weather data,Hadoop MapReduce tool is employed.The proposed solar irradiance prediction model operates on different stages.Primarily,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data normalization.Besides,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive analysis.It is divided into input layer,hidden layer,loadbearing layer,and output layer.To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is applied.In order to validate the performance of the proposed model,a series of experiments was conducted.The experimental values infer that the proposed model outperformed other methods used for comparison.