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• A critical analysis of existing review papers are provided. • Geographical research distribution and gaps on End of life PV modules are depicted. • The scientific landscape of EoL PV panels by publication is mapped. • Bibliometric details of the papers in EoL PV management are tabulated. • Patents trend analysis on the treatment procedures of EoL PV modules is presented. PV modules which are installed worldwide have a defined lifetime for useful service after which the panels become End-of-Life (EoL) products. An enormous amount of obsolete solar PV modules will be added to the waste stream in the near future. Hence, the EoL photovoltaic waste stream could cause an appalling problem in the future if a holistic management strategy is not considered. Despite the vast research on photovoltaic technology, little is known about the perspective of how the EoL PV modules will be handled. The current study systematically investigates global research on EoL PV modules to identify gaps for further exploration. The review reveals that most of the research concentrates on the recovery and recycling of PV panels. Also, the vast majority of the research is mostly carried out in laboratory-scale. The geographical distribution of the studies was concentrated on 15 countries including the USA, Italy, and Taiwan, the latter of which has produced the most publications. Life-cycle-assessment and reverse logistics (RL) are two critical aspects of PV waste management and have only recently received attention from researchers, with 11 and six papers respectively. There are still many countries which have not attempted to forecast their EoL solar-panel waste stream and develop recycling infrastructure. Based on review findings, the future research must be focused on forecasting the PV waste streams, development of recycling technologies, reverse logistics and the policies of individual PV consumer countries. Finally, this study develops a foundation for future research on Photovoltaic waste management to build upon. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) and zenith total delay (ZTD) are highly correlated indicators used for forecasting rainfall effectively. These two factors are widely used when establishing qualitative and quantitative rainfall forecast models. Another indicator, however, convective available potential energy (CAPE), is also highly correlated with the occurrence of rainfall, but has not been combined with PWV or ZTD in previous studies. Therefore, a novel rainfall forecast model based on support vector regression that combines CAPE and Global Navigation Satellite System (GNSS)-derived PWV is proposed in this paper. Moreover, annual, seasonal scales, and hourly autocorrelation for predictors were also considered, and wavelet coherence (WTC) was introduced to further reveal the correlation relationships between CAPE, PWV and rainfall in both time and frequency domains. Hourly PWV, CAPE, temperature (T) and rainfall over a time span of three years were collected from four stations in Singapore and Taiwan to evaluate the performance of the proposed model. The root mean square error (RMSE) and mean absolute error (MAE) between the measured and forecasted rainfall were calculated for each year and averaged. Subsequently, the average RMSE and MAE were determined for the annual seasonal rainfall. The annual average RMSE value was 0.71 mm and the average annual seasonal RMSE value was 0.69 mm. The annual average MAE value was 0.21 mm and the annual seasonal was 0.22 mm. The average correlation coefficient (R2) for the annual value was 0.94 and the annual seasonal R2 value was 0.93. These results indicate that the forecast accuracy at the seasonal scale is basically consistent with annual scale. Additionally, a measure called comparable RMSE (CRMS) was introduced to evaluate the forecasting accuracy across all grades of rainfall. This analysis showed that the accuracy of moderate rainfall (MR) and heavy rainfall (HR) forecasts is similar, but slight rainfall (SR) forecasts are the most accurate among seasons and schemes. • A short-term rainfall forecast method is proposed using convective available potential energy (CAPE) and GNSS-derived PWV. • The forecast accuracy of seasonal scales is consistent with annual scale. • Results showed that the forecast accuracy of moderate rainfall and heavy rainfall were comparable while that of slight rainfall was highest. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, six univariate forecasting models for the container throughput volumes in Taiwan’s three major ports are presented. The six univariate models include the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey model, the hybrid grey model, and the SARIMA model. The purpose of this paper is to search for a model that can provide the most accurate prediction of container throughput. By applying monthly data to these models and comparing the prediction results based on mean absolute error, mean absolute percent error and root mean squared error, we find that in general the classical decomposition model appears to be the best model for forecasting container throughput with seasonal variations. The result of this study may be helpful for predicting the short-term variation in demand for the container throughput of other international ports. [Copyright &y& Elsevier]

Abstract: This paper uses linear and nonlinear statistical models, including artificial neural network (ANN) methods, to investigate the influence of the four economic factors, which are the national income (NI), population (POP), gross of domestic production (GDP), and consumer price index (CPI) on the electricity consumption in Taiwan and then to develop an economic forecasting model. Both methods agree that POP and NI influence electricity consumption the most, whereas GDP the least. The results of comparing the out-of-sample forecasting capabilities of the two methods indicate the following. (1) If given a large amount of historical data, the forecasts of ARMAX are better than the other linear models. (2) The linear model is weaker on foretelling peaks and bottoms regardless the amount of historical data. (3) The forecasting performance of ANN is higher than the other linear models based on two sets of historical data considered in the paper. This is probably due to the fact that the ANN model is capable of catching sophisticated nonlinear integrating effects through a learning process. To sum up, the ANN method is more appropriate than the linear method for developing a forecasting model of electricity consumption. Moreover, researchers can employ either ANN or linear model to extract the important economic factors of the electricity consumption in Taiwan. [Copyright &y& Elsevier]

• Nursing manpower needed for the cancer disease healthcare will increase continuously in the future. • The incoordination between teaching and examination has impact on the nursing manpower supply. • By applying traditional fuzzy set to system dynamic, it replaces the definite values with interval values, making the prediction results more flexible. • Manpower for cancer medical care demand is calculated. • Manpower for cancer medical care supply is estimated through data analysis. This paper presents a dynamic model aimed at predicting nursing manpower requirements for cancer care over the next ten years. The proposed model, based on the Taiwan Health Insurance Database (2000 to 2010), is meant to serve as a reference in establishing policy for government health units. The proposed prediction model uses fuzzy sets to replace definite values with interval values in order to account for uncertainties in real-world data and enhance the flexibility of prediction results. Our results suggest that the demand for nursing manpower for cancer care will grow steadily in the foreseeable future. The gap between the demand for nursing staff and the supply is expected to peak in 2027. By then, the number of oncologists is expected to reach 7,083 (54.32% of the total number of in-hospital physicians), but the number of oncology nurses will be less than 26,297 (56.5% of the total healthcare manpower). It is also expected that there will be fewer than 1,613 outpatient physicians (71.81% of the total number of physicians) and fewer than 4,967 outpatient nurses (68.46% of the total nursing manpower). This paper provides a valuable reference for government agencies involved in the nursing manpower planning to improve the quality of nursing care. [ABSTRACT FROM AUTHOR]

The shear wave velocity profile and dynamic soil properties are known to be affected by aleatory uncertainty. This paper aims to investigate the effect of a statistical variation in the initial stiffness profile, stiffness degradation and damping curves on ground response predictions by conducting stochastic analysis. The Large Scale Seismic Test site in Lotung, Taiwan, is back-analysed with a fully-coupled finite element procedure using an advanced kinematic hardening soil model. Two ground motions recorded at the site, one strong and one weak, are applied at bedrock level. The results reveal that the site response prediction is sensitive to the seismic intensity of the input motion. When the level of induced shear strain is higher, i.e. in the case of the stronger motion, the spatial variability of the stiffness degradation and damping curves has a pronounced effect on the predicted site response. In contrast, when the weaker motion is considered the prediction is particularly sensitive to the statistical variation in the initial stiffness profile. This is mainly due to the stiffness degradation at very small strains shown by the laboratory data on LSST soils, which is captured in the paper by assuming an appropriate elastic domain in the constitutive model calibration. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) method for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experimental results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively. [Copyright &y& Elsevier]

Abstract: Accurate and immediate forecast in the short product life cycle of semiconductor market is difficult, but important. This paper proposed a grey model with factor analysis techniques to deal with the multi-factor forecasting problems. In the grey modeling, the use of genetic algorithm has the ability to search global optimum solution to construct two improved multivariable grey forecasting models that are AGAGM(1,N) and GAGM(1,N). These two models are applied for forecasting Taiwanese integrated circuit output. The results of the factor analysis show that the major factors of Taiwan’s integrated circuit output comprise R&D intensity, foreign investment, index of industrial production, trade specialization coefficient, and intra-industry trade coefficient. The improved multivariable grey forecasting models are found to be feasible and effective. [Copyright &y& Elsevier]

Abstract: In our daily life, we often use some forecasting techniques to predict weather, temperature, stock, earthquake, economy, etc. Based on these forecasting results, we can prevent damages to occur or get benefits from the forecasting activities. In fact, an event in the real-world can be affected by many factors. The more the facts we consider, the higher the forecasting accuracy rate. Moreover the length of each interval in the universe of discourse also affects the forecasting results. In this paper, we present a new method to predict the temperature and the Taiwan Futures Exchange (TAIFEX), based on automatic clustering techniques and two-factors high-order fuzzy time series. The proposed method gets higher average forecasting accuracy rates than the existing methods. [Copyright &y& Elsevier]

Abstract: A typhoon-surge forecasting model was developed with a back-propagation neural network (BPN) in the present paper. The typhoon''s characteristics, local meteorological conditions and typhoon surges at a considered tidal station at time t−1 and t were used as input data of the model to forecast typhoon surges at the following time. For the selection of a better forecasting model, four models (Models A–D) were tested and compared under the different composition of the above-mentioned input factors. A general evaluation index that is a composition of four performance indexes was proposed to evaluate the model''s overall performance. The result of typhoon-surge forecasting was classified into five grades: A (excellent), B (good), C (fair), D (poor) and E (bad), according to the value of the general evaluation index. Sixteen typhoon events and their corresponding typhoon surges and local meteorological conditions at Ken–fang Tidal Station in the coast of north-eastern Taiwan between 1993 and 2000 were collected, 12 of them were used in model''s calibration while the other four were used in model''s verification. The analysis of typhoon-surge forecasting results at Ken–fang tidal station show that the Model D composing 18 input factors has better performance, and that it is a suitable BPN-based model in typhoon-surge forecasting. The Model D was also applied to typhoon-surge forecasting at Cheng-kung Tidal Station in south-eastern coast of Taiwan and at Tung-shih Tidal Station in the coast of south-western Taiwan. Results show that the application of Model D in typhoon-surge forecasting at Cheng-kung Tidal Station has better performance than that at Tung-shih Tidal Station. [Copyright &y& Elsevier]

• The online monitoring system to detect the deteriorative insulators by predicting the leakage current. • The performance of predicting leakage current by utilizing the surface spark discharge features significantly outperform the persistent data models. • The particle swarm optimization combined neural network algorithm surpasses other persistent model in predicting leakage current. • The change of ROI brightness has stronger correlation with the leakage current comparing with the percentage of spark area. The paper has completely built an online monitoring system to detect deteriorative insulator by predicting the leakage current which assist maintenance personnel in determining whether insulators need to be cleaned or replaced and improve maintenance efficiency in Taiwan. The real-time monitoring systems are installed on the 69 kV and 161 kV transmission towers. The local meteorological data such as temperature, humidity and dew point, and images of spark discharge phenomenon are collected to identify the correlation with the leakage current of deteriorative insulators. The forecasting model is enhanced by using the image features of surface spark discharge phenomenon (SSDP). The percentage of spark area and the change in the brightness of region of interest (ROI) can be utilized as exogenous inputs to the predicting model. Correspondently, a particle swarm optimization based back propagation neural network (PSO-BPNN) is applied to improve the accuracy in prediction. The proposed developing model combined enhancement inputs are compared with persistent models, such as back propagation neural network (BPNN), radial basis function neural network with K-means cluster (RBF-K-means) and support vector regression (SVR), to evaluate the performance based on common statistic metrics. The simulation experiments have proved the integrated surface spark discharge features combined PSO-BPNN to improve the accuracy and stability of predicting leakage current comparing with other models. Therefore, the surface spark discharge data have strong correlation with the leakage current. In addition, the PSO-BPNN also achieves higher accuracy, better effectiveness and faster convergence comparing with other persistent models. [ABSTRACT FROM AUTHOR]

Recently, the prediction of wind power generation has served as a prerequisite problem for determining the objective of the reliability and low–cost power system performance. The difference between conventional deterministic prediction and probabilistic prediction is that the first types ignore the uncertainty of the wind speeds. In this paper, a novel probabilistic prediction method as improved lower–upper bound approximation is applied for building the reliable prediction intervals, which is based on the system that uses the mathematical models of the environment to prognosticate the weather based on existing meteorology (Numeral Weather Prediction System). To verify the worth of the proposed method, forecasting modes in this study are divided as hourly and daily predictions, which are applied to seven wind farms of Taiwan. In addition, adapting parameters in improved lower–upper bound approximation method is performed by resorting to the charged search system algorithm. Also, to increase the prediction speed, the probability density function of wind speed is used, and to decrease the estimation model volume, the kernel density estimation model is applied into the proposed method. The simulation results in comparison with other methods indicate that the prediction intervals produced by the proposed method outperformed and its indices are better. The average error between prediction and real data in the proposed method obtained as about 11%, while other methods error is more than 15%. In addition, three prediction interval indices are used in order to evaluate the different studied manners as: prediction interval overlay probability, normalized average of prediction interval length, and overlay width scale. As the results shown, the best performance is related to the proposed method. Image 1 • Using of the developed LUBA for forecasting the wind power in both daily and hourly modes. • Using of the CSSA for optimization of the model parameters in the ILUBA. • Applying the KDE method to decrease the NWPs data dimension. • Using of the PDF to create the wind speed ensemble model for increasing the prediction speed at the all processing states. • Verify the proposed model by applying it in the real–wind farms. [ABSTRACT FROM AUTHOR]