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China wishes to put more efforts in achieving its energy saving and emission reduction targets, for which photovoltaic (PV) power generation has showed great potential in many economic entities around the world. China also shows a strong sign of renewable energy booming: wind power and photovoltaic installed capacity soared in recent years. Actually, many countries around the world especially European countries have a longer history than China in PV application. But China has a unique energy industry development route, which may lead to a different PV prospective. To determine the uniqueness of China׳s PV development, data from China and 26 Organization for Economic Cooperation and Development (OECD) countries are collected. The Characteristics of China׳s PV industry is quantified and extracted in this paper. Indicators are selected from aspects of energy source, energy generation and energy consumption. Indicator distribution analysis, correlation analysis, and country cluster analysis methods are applied. 3 major characteristics of China׳s PV industry are extracted. Based on the key findings of this paper, it can be concluded that some inertial elements in the energy industry may be the biggest obstacles for China׳s PV development, and off-grid and distributed PV solutions are suggested to be better ways than concentrated PV power generation. [ABSTRACT FROM AUTHOR]

In this paper a spatio-temporal analysis of wind power resource in the Iberian Peninsula is presented. The study uses the Second-Order Data-Coupled Clustering (SODCC) algorithm over reanalysis data in the for the period 1979 – 2014. Several characteristics of the method are detailed, such as the data-coupled clustering approach of SODCC, that ensures the non-singularity of the signal subspace within each cluster. The performance of the proposed approach and specific results obtained have been discussed in a case study in the Iberian Peninsula. In these results it is possible to identify different spatio-temporal patterns of the wind data statistics depending on the initialization year. Moreover, this work also shows that there is a close relationship between these spatio-temporal patterns with the wind energy production of the area under study, so the proposed analysis can be extended to wind farms efficiency production at the time scales considered. [ABSTRACT FROM AUTHOR]

The selection of training data for establishing a model directly affects the prediction precision. Wind power has the characteristic of daily similarity. The corresponding meteorological data also has the characteristic of daily similarity. This paper proposes a new model with cluster analysis of the numerical weather prediction information. The similar day with the predicted day is searched as training sample to a generalized regression neural network model. The numerical weather prediction data and actual wind power data from a wind farm are used in this study to test the model. The prediction results show that correct cluster analysis method is a useful tool in day-ahead wind power prediction. [ABSTRACT FROM AUTHOR]

Renewable energy development is a major response to address the issues of climate change and energy security. The utilization of renewable resources, however, highly depends on the climate conditions, which may be impacted in the future due to global climate change. Based on literature analysis, this paper presents a general framework for renewable energy vulnerability assessment and applies grey cluster analysis method to demonstrate the features of vulnerability, and then employs the Simple Additive Weighting Approach to address the multiple-attribute decision problems of vulnerability assessment in China. The categorized results imply that the proposed index system is suitable for decision-making analysis and comparative analysis of renewable energy vulnerability to climate change in China. In terms of exposure part, the cluster results are complex and varied due to the unique combination of natural factors, social factors and the energy structure. In the sensitivity section, the results are presented on the distribution of areas rich in hydropower, wind power and solar energy potential. Moreover, the main results of this study are the higher renewable energy vulnerability of the poorer regions of China to climate change and the relative higher importance of adaptive capacity building in vulnerability management. Finally, policy recommendations on regional renewable energy vulnerability management are also made. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to analyze consumers’ awareness and attitudes towards biofuels implementation in road transportation sector of China with respect to different consumption groups. Firstly, the status and future trends have been reviewed, which focuses on the private vehicles development, energy demands of road transportation sector and biofuels. Furthermore, a comprehensive survey has been conducted for 374 private vehicle drivers (owners) in Nanjing. A questionnaire survey method is implemented to collect information including respondents’ awareness, brief of biofuels, selection attitudes to biofuels and views on policies. Furthermore, the biofuels selection motives of respondents have been concluded into two main factors, “purchasing pressures” and “product attraction”. Then the respondents were divided into three clusters and compared. The results indicate that 90% of the respondents need to know more about biofuels implementation, and the majority of the respondents emphasize the active significance of reducing GHG emissions to improve energy security and mitigate energy crisis. But 55.6% of the respondents think that biofuels technology is still not mature. It is found that the different perceptions of biofuels between 192 passenger vehicle drivers (PVD) and 182 freight vehicle drivers (FVD). The former focuses on fuel price and fuel performance, while the latter pays more attention to fuel availability. Besides, PVD concentrate on fuel-related attributes while FVD focus on policy-related factors. The survey results give rise to some deeply consideration about the various preferences and awareness on biofuels among different consumer groups. These findings in this study will help to understand the consumer''s energy consumption behaviors and improve the further implementation for biofuels in Nanjing city. [Copyright &y& Elsevier]

In recent years, renewable and sustainable energy sources have attracted the attention of various investors and stakeholders, such as energy sector agents and even consumers. It is perplexing to observe and anticipate the required levels of photovoltaic generation, which are inherent tasks for such rapid insertion into the electric grid. This distributed/renewable generation must be integrated in a coordinated way such that there is no negative impact on the electric performance of the grid, increasing in the complexity of energy management. In this article, a methodology for photovoltaic generation forecasting is addressed for a horizon of one week ahead, using a new approach based on an artificial neural network (ANN) ensemble. Two main questions will be explored with this approach: how to select the ANNs, and how to combine them in the ensemble. The design of experiments (DOE) approach is applied to the photovoltaic time series factors and ANN factors. Then, a cluster analysis is performed to select the best networks. From this point on, a mixture (MDE) is employed to determine the ideal weights for the ensemble formation. The methodology is detailed throughout the paper and, based on the combination of forecasts, the photovoltaic generation was estimated for a specific panel set located in the state of Minas Gerais, Brazil, reaching the value of 4.7% for the weekly mean absolute percentage error. The versatility of the proposed method allowed the change of the number of factors to be used in the experimental arrangement, the forecast model, and the desired forecast horizon, and consequently enhancing the forecasting determination. • An ANN ensemble is created for medium-term photovoltaic generation forecasting. • ANNs are selected by using a new design of experiments approach. • The ANN ensemble is built by using a new mixture design of experiments approach. • We predicted the photovoltaic generation for a panel set located in Brazil. [ABSTRACT FROM AUTHOR]