Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models

Deep learning for pattern recognition of photovoltaic energy generation

A smart fault detection approach for PV modules using Adaptive Neuro-Fuzzy Inference framework

The use of renewable energy to reduce the effects of climate change and global warming has become an increasing trend. In order to improve the prediction ability of renewable energy, various prediction techniques have been developed. The aims of this review are illustrated as follows. First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy predictions. Thirdly, the analysis of sources of renewable energy, values of the mean absolute percentage error, and values of the coefficient of determination were conducted. Finally, some possible potential opportunities for future work were provided at end of this survey.

A Survey of Machine Learning Models in Renewable Energy Predictions

The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

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Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

For electronic products, printed circuit boards are employed to fix integrated circuits (ICs) and connect all ICs and electronic components. This allows for the smooth transmission of electronic signals among electronic components. Machine learning (ML) techniques are popular and employed in various fields. To capture the nonlinear data patterns and input–output electrical relationships of analog circuits, this study aims to employ ML techniques to improve operations from modeling to testing in the analog IC packaging and testing industry. The simulation calculation of the resistance, inductance, and capacitance of the pin count corresponding to the target electrical specification is a complex process. Tasks include converting a two-dimensional circuit into a three-dimensional one in simulation and modeling-buried structure operations. In this study, circuit datasets are employed for training the ML model to predict resistance (R), inductance (L), and capacitance (C). The least squares support vector regression (LSSVR) with Genetic Algorithms (GA) (LSSVR-GA) serves as an ML model for forecasting RLC values. Genetic algorithms are used to select parameters of LSSVR models. To demonstrate the performance of LSSVR models in forecasting RLC values, three other ML models with genetic algorithms, including backpropagation neural networks (BPNN-GA), random forest (RF-GA), and eXtreme gradient boosting (XGBoost-GA), were employed to cope with the same data. Numerical results illustrated that the LSSVR-GA outperformed the three other forecasting models by around 14.84% averagely in terms of mean absolute percentage error (MAPE), weighted absolute percent error measure (WAPE), and normalized mean absolute error (NMAE). This study collected data from an IC packaging and testing firm in Taiwan. The innovation and advantage of the proposed method is using a machine approach to forecast RLC values instead of through simulation ways, which generates accurate results. Numerical results revealed that the developed ML model is effective and efficient in RLC circuit forecasting for the analog IC packaging and testing industry.

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RLC Circuit Forecast in Analog IC Packaging and Testing by Machine Learning Techniques

Due to rapid development in information technology in both hardware and software, deep neural networks for regression have become widely used in many fields. The optimization of deep neural networks for regression (DNNR), including selections of data preprocessing, network architectures, optimizers, and hyperparameters, greatly influence the performance of regression tasks. Thus, this study aimed to collect and analyze the recent literature surrounding DNNR from the aspect of optimization. In addition, various platforms used for conducting DNNR models were investigated. This study has a number of contributions. First, it provides sections for the optimization of DNNR models. Then, elements of the optimization of each section are listed and analyzed. Furthermore, this study delivers insights and critical issues related to DNNR optimization. Optimizing elements of sections simultaneously instead of individually or sequentially could improve the performance of DNNR models. Finally, possible and potential directions for future study are provided.

A Study of Optimization in Deep Neural Networks for Regression

Since the outbreak of the Coronavirus Disease 2019 (COVID-19), the spread of the epidemic has been a major international public health issue. Hence, various forecasting models have been used to predict the infectious spread of the disease. In general, forecasting problems often involve prediction accuracy decreasing as the horizon increases. Thus, to extend the forecasting horizon without decreasing performance or prediction, this study developed a Dual Long Short-Term Memory (LSTM) with Genetic Algorithms (DULSTMGA) model. The model employed predicted values generated by LSTM models in short-forecasting horizons as inputs for the long-term prediction of LSTM in a rolling manner. Genetic algorithms were applied to determine the parameters of LSTM models, allowing long-term forecasting accuracy to increase as long as short-term forecasting was accurate. In addition, the compartment model was utilized to simulate the state of COVID-19 and generate numbers of infectious cases. Infectious cases in three countries were employed to examine the feasibility and performance of the proposed DULSTMGA model. Numerical results indicated that the DULSTMGA model could obtain satisfactory forecasting accuracy and was superior to many previous studies in terms of the mean absolute percentage error. Therefore, the designed DULSTMGA model is a feasible and promising alternative for forecasting the number of infectious COVID-19 cases.

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Jung Pin Lai

Papers

A Survey of Machine Learning Models in Renewable Energy Predictions

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

RLC Circuit Forecast in Analog IC Packaging and Testing by Machine Learning Techniques

A Study of Optimization in Deep Neural Networks for Regression

A Dual Long Short-Term Memory Model in Forecasting the Number of COVID-19 Infections