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Abstract Long-term wind power forecasting is a challenging endeavor that requires predictions that span years into the future. Accurate forecasting is crucial for optimizing energy production, grid integration, maintenance scheduling, and financial planning. This study attempts to first develop the long short-term memory networks (LSTM) with a seasonal wavelet transform forecasting model for practical long-term wind power forecasting problems with seasonal and regional influences on wind power and the instability of data signals. This model encapsulates wavelet transformation and seasonal decomposition, and employs LSTM for forecasting. The new prediction model adopted seasonal decompositions and two LSTMs to approach low- and high-frequency series datasets, as well as the wavelet synthesis prediction values. Furthermore, the parameters of the LSTM models are selected using stochastic optimization. For a comprehensive evaluation, the proposed LSTM with seasonal wavelet transform is compared with seven methods, including seasonal LSTM (SLSTM), wavelet LSTM (WLSTM), and the seasonal auto-regressive integrated moving average (SARIMA), back propagation neural network (BPNN), generalized regression neural network (GRNN), least square support vector regression (LSSVR), and support vector regression (SVR) were employed for long-term wind power output forecasting of wind farms. The empirical results underscore that the performance of the proposed forecasting model is better than other methods in terms of forecasting accuracy, which could efficiently provide reliable long-term predictions for long-term wind power output forecasting.

This study attempts to propose an availability optimization decision support design system for repairable n-stage mixed systems, in which different combinations of subsystems, such as parallel, standby, and k-out-of-q, are connected in series configuration. The enumeration method, tabu search, simulated annealing, non-equilibrium simulated annealing, and the modified redundancy allocation heuristic combined with a modified genetic algorithm will be proposed to solve the system availability optimization problem and further determine the appropriate system configuration design. Several simulated cases are conducted by following the procedural flow of the proposed availability optimization decision support design system to reach the optimal allocations of the component redundancy amount, the optimal repair rates, and the optimal failure rates of all subsystems to minimize the total system cost under several configuration constraints for different repairable n-stage mixed systems. Simulated results display that the proposed availability optimization decision support design system can definitely take advantage of different component redundancy system designs, including the parallel-series system, n-stage standby system, n-stage k-out-of-q system, and n-stage mixed system, to save a lot of cost and meet the high level of the system availability requirement compared to the n-stage single component series system. Additionally, the results for all proposed combined methods also show that the parallel-series system can obviously reach the same level of system availability requirement with less system total cost, in contrast to the n-stage standby system, by presuming the identical deteriorating probability for both the operating components and the standby components. The performance comparisons of five proposed combined methods for four proposed system configurations are analyzed comprehensively. It can be concluded that the performances of the modified redundancy allocation heuristic method, combined with a modified genetic algorithm on the criteria of the optimal system costs for four proposed system configurations, are not only superior to the other four combined methods, but also to the performances on the criteria of CPU running time for four proposed system configurations.

Wind is one of the most essential sources of clean, renewable energy, and is, therefore, a critical element in responsible power consumption and production. The accurate prediction of wind speed plays a key role in decision-making and management in wind power generation. This study proposes a model using a deep belief network with genetic algorithms (DBNGA) for wind speed forecasting. The genetic algorithms are used to determine parameters for deep belief networks. Wind speed and weather-related data are collected from Taiwan's central weather bureau for this purpose. This paper uses both time series data and multivariate regression data to forecast wind speed. The seasonal autoregressive integrated moving average (SARIMA) method and the least squares support vector regression for time series with genetic algorithms (LSSVRTSGA) are used to forecast wind speed in a time series, and the least squares support vector regression with genetic algorithms (LSSVRGA) and DBNGA models are used to predict wind speed in a multivariate format. The empirical results show that forecasting wind speed by the DBNGA models outperforms the other forecasting models in terms of forecasting accuracy. Thus, the DBNGA model is a feasible and effective approach for wind speed forecasting.

This paper develops a novel kernel intuitionistic fuzzy rough set (KIFRS) model as a hybrid model to improve the effects of rule generation based on rough sets. The KIFRS model adopts new kernel intuitionistic fuzzy clustering (KIFCM) to enhance the performance of rough set theory (RST). To effectively improve the rule generation based on RST, the proposed hybrid method first adopts KIFCM to cluster raw data into similarity groups. Based on the KIFCM results, the RST can obtain superior performance in generating rules. Two benchmark machine learning data sets from the UCI machine learning repository are used to examine the performance of the developed model. The results show that the KIFRS model achieves superior performance to those of the traditional decision tree and rough set models.

To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan’s wind power output datasets.

Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

This study attempts to consider CO2 emission cost to propose an online distribution system of inventory-routing problem with simultaneous deliveries and returns (IRPSDRCO2). The proposed IRPSDRCO2 mathematical models will be developed to find the total inventory routing cost, the CO2 emission cost, the optimal delivery routes, the economic order quantities, the optimal reordering points, the optimal service levels, the optimal common review interval, and the optimal maximum inventory levels for all retail stores delivered in these scheduled routes. The proposed IRPSDRCO2 model further applies the savings method, the target insert heuristic method, the target exchange heuristic method, and the repeated target heuristic method to find the optimal solution and the related scheduled routing plan. The sensitivity analyses will be conducted for this proposed IRPSDRCO2 model based on different parameters to provide very helpful decision-making information for a distribution system design. The performance of the proposed repeated target heuristic method is demonstrated to be better than most of four other combination methods regarding both the inventory routing cost and CPU running time. Consequently, it should be very helpful for logistics firms to design their distribution system by following the structure and the detailed procedure flow of the online distribution system developed by this study.

The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts’ knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

The electronics industry in Taiwan has achieved a complete information and communication technology chain with a firm position in the global electronics industry. The integrated-circuit (IC) packaging industry chain adopts a professional division of labor model, and each process (including wafer dicing, die bonding, wire bonding, molding, and other subsequent processes) must have enhanced process capabilities to ensure the quality of the final product. Increasing quality can also lower the chances of waste and rework, lengthen product lifespan, and reduce maintenance, which means fewer resources invested, less pollution and damage to the environment, and smaller social losses. This contributes to the creation of a green process. This paper developed a complete quality evaluation model for the IC packaging molding process from the perspective of a green economy. The Six Sigma quality index (SSQI), which can fully reflect process yield and quality levels, is selected as a primary evaluation tool in this study. Since this index contains unknown parameters, a confidence interval based fuzzy evaluation model is proposed to increase estimation accuracy and overcome the issue of uncertainties in measurement data. Finally, a numerical example is given to illustrate the applicability and effectiveness of the proposed method.