Deep learning models for solar irradiance forecasting: A comprehensive review

Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

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Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction

Artificial intelligence (AI) technology adoption in the Philippines can be seen widely in many sectors such as industry, agriculture, and services. AI may have a negative effect, but AI can generate more employment in all economic sectors through proper talent training. To compensate for the detrimental effects of the implementation of AI, everyone must have enough understanding of the technology's positive benefits. In order to respond to the demands of the industries, the public and private sectors must cooperate with government and academe to develop the correct skill sets of graduates and workers.

Artificial Intelligence: The Technology Adoption and Impact in the Philippines

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A Web-Based Model to Predict a Neurological Disorder Using ANN

Energy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.

Using Stacked Long Short Term Memory with Principal Component Analysis for Short Term Prediction of Solar Irradiance based on Weather Patterns

Fault detection and monitoring system in photovoltaic (PV) energy management system is important in achieving its optimal performance. An effective diagnostic system involves correct analysis of electrical parameters of a PV array on a given weather condition. In the study, mean-shift clustering was applied for pre-classification and anomaly detection of time-series data of electrical parameters from grid-tied inverter, and solar-irradiance. Classification and anomaly detection applied is based in ensemble learning, where its base learners are based from multilayer perceptron. A stacking ensemble is used in classification of energy production profile while bagging ensemble is used detecting anomalous trend in time-series data. A stacking ensemble got a highest accuracy value of 94% compared to single classifiers which have accuracy value of 85.25%, 84.14%, and 63.4%, respectively. The bagging ensemble autoencoders have the lowest mean squared error during model reconstruction compared to single autoencoder. It has a fair performance in classifying anomaly points from normal datapoints, having an AUC value of 0.795 and F1-score of 0.71, given that the hyperparameter is 0.5. Overall, ensemble learners improve the performance in classification and detection tasks.

Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System

Energy production of photovoltaic (PV) system depends on the amount of solar irradiance present on a certain location. Accurate prediction of solar irradiance ensures economic integration of PV system to grid and leads to optimal dispatching of available energy resources. Weather conditions has strong correlation with solar irradiance, and its erratic nature causes fluctuation to energy production. Therefore, it is difficult to achieve consistent optimal energy production and reliable prediction of solar irradiance. In the study, a bagging-based ensemble learning system was used to predict solar irradiance based on weather patterns. Previous researches confirmed that ensemble learners produced unbiased and more accurate results compared to single learners. A pre-processed stacked long-short term memory model (stacked LSTM) was used as base learner in ensemble learning since it has good performance in handling time series sequences. A plot that compares the performance between single learner and ensemble learners was provided. From the plot, it shows that at some iteration, ensemble learners get consistent at providing more accurate predictions compared to single learners. Metrics used in the study include explained variance score, maximum residual error, mean absolute error, mean squared error, and regression score function.

Solar Irradiance Prediction Based on Weather Patterns Using Bagging-Based Ensemble Learners with Principal Component Analysis

The study aims to design an intelligent total body water measuring device which will help to determine the total body water level or percentage of an individual using ultrasonic sensor, load cell and bioelectric impedance analysis (BIA). The system utilized the Scaled Conjugate Gradient Artificial Neural Network (ANN) as the machine learning algorithm. The system used the dataset splitting of 70%-15%15% for training, validation and testing. Different hidden neurons were used and compared during neural network training and found out that using 10 neurons will provide the lowest mean square error (MSE) with best value of Pearson’s correlation (R). Based on the results, using 10 neurons, Scaled Conjugate Gradient algorithm has better performance as compared to Levenberg-Marquardt algorithm with MSE equal to 0.180033, 0.118954, 0.529157 while the R value is equal to 0.997887, 0.997488, 0.99644 for training, validation and testing.

Prediction of Total Body Water using Scaled Conjugate Gradient Artificial Neural Network

Batteries are very important in many different applications. In the solar energy system, the batteries are used as power storage when solar energy is not available especially during night time. Batteries need to be maintained and closely monitor their condition. Battery management systems are normally used for this application but many of them are not monitoring the battery’s temperature. This study will use a fuzzy logic-controlled system to manage the operation of the battery. This system will maintain the operation of the battery in the allowed operating temperature to prevent it from damaged caused by excessive internal temperature.

Hilario A. Calinao

Papers

Using Stacked Long Short Term Memory with Principal Component Analysis for Short Term Prediction of Solar Irradiance based on Weather Patterns

Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System

Solar Irradiance Prediction Based on Weather Patterns Using Bagging-Based Ensemble Learners with Principal Component Analysis

Prediction of Total Body Water using Scaled Conjugate Gradient Artificial Neural Network

Battery Management System with Temperature Monitoring Through Fuzzy Logic Control