You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Energy big data: A survey 
Chapter Title:  A. RENEWABLE ENERGY 
1. Abstract of Cited Paper (BIB001):  Wind energy is considered to be a very promising alternative for power generation because of its tremendous environmental, social, and economic benefits. Electrical power generation from wind energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of those facilities, therefore, affect the power system reliability in a manner different from that of the conventional systems. This paper is focused on the development of suitable models for wind energy conversion systems, in adequacy assessments of generating systems, using wind energy. These analytical models can be used in the conventional generating system adequacy assessment utilizing analytical or Monte Carlo state-sampling techniques. This paper shows that a five-state wind energy conversion system model can be used to provide a reasonable assessment of the practical power system adequacy studies, using an analytical method, or a state-sampling simulation approach. 
2. Abstract of Cited Paper (BIB002):  Against the backdrop of increasing energy demands, the threat of climate change and dwindling fuel reserves, finding reliable, diverse, sustainable/renewable, affordable energy resources has become a priority for many countries. Marine energy conversion systems are at the forefront of providing such a resource. Most marine renewable energy conversion systems require tribological components to convert wind or tidal streams to rotational motion for generating electricity while wave machines typically use oscillating hinge or piston within cylinder geometries to promote reciprocating linear motion. This paper looks at the tribology of three green marine energy systems, offshore wind, tidal and wave machines. Areas covered include lubrication and contamination, bearing and gearbox issues, biofouling, cavitation erosion, tribocorrosion, condition monitoring as well as design trends and loading conditions associated with tribological components. Current research thrusts are highlighted along with areas needing research as well as addressing present-day issues related to the tribology of offshore energy conversion technologies. 
3. Abstract of Cited Paper (BIB003):  Energy efficiency has been increasingly discussed around the world due to the perception of its contribution to many issues like energy consumption, carbon emissions and others. In this context, demand side management is the most common way for energy efficiency improvement and consequent energy saving. However, in a changing scenario where renewable sources play an important role in energy supply, new approaches to energy efficiency looking to supply side may be very helpful on achieving a big amount of energy savings that can be added to those expected from demand side. This paper intends to propose a methodology for calculation and measurement of the overall energy efficiency for biomass cogeneration or combined heat and power plants. The calculation and measurement methods are focused on the principle of so called measurement and verification. The application of the proposed methodology can lead to continuous efficiency improvement programs both for cogeneration plants and generator groups connected to a grid. Its benefits are even beyond the generation agents purposes. Once generators develop a new paradigm of transparency, with real time performance data that can be accessed by other interested agents, such as customers or grid administrators, a contribution to the concept of smart grids will be made whilst relevant supply side performance information is disclosed to other parties. 
4. Abstract of Cited Paper (BIB004):  Using learning curves as an analytical tool for technology forecasting involves making assumptions over a range of key uncertainties, often implicitly. In this paper, we present an explicit treatment of the key uncertainties involved in learning rates' analyses of marine energy innovation (wave and tidal stream) — technology fields attracting considerable interest, but whose commercial prospects depends on substantial learning and cost reduction. Taking a simple single factor learning rate model, we describe a range of plausible learning investments required so that marine energy technologies become cost-competitive with their ‘benchmark’ technology: offshore wind. Our analysis highlights the sensitivity of marine energy to three key parameters: the capital cost of first devices, the level of deployment before sustained cost reduction emerges, and the average rate of cost reduction with deployment (learning rate). Figures often quoted within the marine energy sector for the parameters of starting cost, learning rate, and capacity at which sustained cost reduction occurs (metrics conventionally used for learning rate analysis) can be seen to represent very attractive scenarios. The intention of this paper is to display that even small changes to input assumptions can have a dramatic effect on the overall investment required for a sector to reach parity with benchmark technologies. In the short term, reaching cost competitiveness with offshore wind is a necessity if marine energy is to reach commercialisation. Additionally, an assessment of the plausible total investment (and inherent uncertainties) in a global wave and tidal deployment scenario will be presented. The paper also considers the implications of these uncertainties for marine energy innovation management. While the benchmark against offshore wind will generally be used as a performance indicator, in order to achieve similar and sustained cost reductions to other, more mature, renewable energy technologies (and thus achieve a competitive price for marine technologies, securing their place within the energy mix), the marine energy sector needs a targeted innovation focus to fulfil the desired objectives, and a development pathway very different to offshore wind must be used. 
5. Abstract of Cited Paper (BIB005):  Owing to the over-exploitation of fossil fuels, many governments have been promoting renewable energy to resolve the limitation of fossil energy and environmental problems. Nevertheless, most of the electricity data lack of systematic analysis to provide useful information. Furthermore, due to the development of cloud technology, these big data vary in type and time. Without appropriate big data analysis and user interface, data would provide error messages. Besides, few of the websites are built for enterprise to provide suggestion as a recommender. In summary, this study intends to develop a recommender system including cloud data base, analytical module and user interface. Based on continuous Markov chain, we analyze data according to the historical electricity data; through time series analysis and multi-objective programming models, a long-term investment of renewable energy decision supports and the best combination of renewable energy can be revealed. The research integrates these modules to construct an enterprise-oriented cloud system. To ensure the effectiveness of the platform, validation test will be performed. The result demonstrates that the recommender system can be used to assist the company in making the best investment of renewable energy and the best combination of energy consumption.

Literature Review:
The new and renewable energy are integrated in the electric power generation, which is different from the traditional power generation mode. This difference causes the measurement and management of generated data from electric power become increasingly difficult and complex. Since the big data technology helps to make better prediction, management and processing complex big data in the energy domain, it has become increasingly popular and widely applied in renewable energy companies once emerging. Many researches have launched on various branches of renewable energy. Paro and Fadigas BIB004 proposed a methodology to measure and calculate the overall biomass energy efficiency. This methodology can be applied in smart programs about regeneration plants and generator groups connected to a grid which will improve the efficiency continuously. MacGillivray et al. BIB005 concentrated their attention on marine energy. The marine energy have attracted considerable interest but its commercial prospects mainly depend on cost reduction and substantial learning. Therefore, the VOLUME 4, 2016 authors presented an explicit solution on the key uncertainties involved in marine energy learning rates analyses including wave and tidal stream. They provided a simple learning model and describe a range of learning investments which made marine energy technologies become cost-competitive. Wool et al. BIB003 looked at the tribological design of three green marine energy systems: tidal, offshore wind, and wave machines. They pointed out that most marine renewable energy conversion systems need tribological components to promote rotational motion of wind or tidal streams to generate electricity. Therefore, they studied and highlighted current research thrusts to address present issues related to the tribology of marine energy conversion technologies. As for another important renewable energy sources, wind energy has also attracted wide attention. For instance, in order to improve the efficiency of electric power generation, the Vestas Wind Technology Group of Denmark optimized the wind turbine geographical placement by using big data technologies to analyze the data information including weather reports, tidal conditions, satellite images and geographical information BIB002 . Besides, they also updated the underlying architecture for better data collection and used big data processing technologies to meet their high-performance computing needs. Furthermore, Kaldellis BIB001 proposed an optimum autonomous wind power system sizing for remote consumers by using long-term wind speed data. Aiming to improve the life quality level of remote consumers, they used the proposed autonomous wind power systems to address the electricity demand requirements, especially in high windpotential locations. All researches above in different application areas have a common purpose: cost minimization. In addition, owing to the over-exploitation of fossil fuels, energy becomes precious and energy consumption problem has arisen wide attention. Kung and Wang BIB006 proposed a recommender system for the best combination of renewable energy resources with cost-benefit analysis, which include analytical module, cloud data base, and user interface. This study used Markov Chain to investigate the influences of decision-making related to renewable energy and electricity demand in random time. Since the historical electricity data is recorded in continuous time series, Continuous Markov Chain can be applied to analyze energy big data in order to help power enterprises make optimal investment of renewable energy and evaluate optimal energy configuration.