Maintenance costs for wind power plants are a significant part of the total life cycle cost, especially for offshore wind power plants, situated at remote sites. In order to decrease the cost of maintenance, monitoring systems have been used to estimate the condition of critical components in wind turbines. This paper proposes Life Cycle Cost analysis (LCC) approach for maintenance management of wind turbines. The LCC approach for maintenance management presented in this paper is an extension on previous work by J. Nilsson and L. Bertling, where a comparison has been made with this previous work and the same is extended with new data and models. Case studies are presented based on data from three different wind turbinesrated 3 and 6MW. Three different scenarios have been studied and the effect of condition monitoring system (CMS) has been analysed. For any chosen value the CMS proves to be a profitable option.
Greenhouse gas emissions from homes arise primarily from fossil fuels burned for heat, the use of products that contain greenhouse gases, and the handling of waste. Human activities are responsible for almost all of the increase in greenhouse gases in the atmosphere over the last 150 years. The household sector is one of the biggest aggregate consumers and this is the reason why increasingly policies have been considering it. One of the key factors in curbing energy consumption in this sector is widely recognized to be due to erroneous behaviors and systems malfunctioning, mainly explained by the lack of awareness of the final user; so, training the final user to energy awareness can be more effective and cheaper than other policies. In this context, energy management in homes is playing, and will play even more in future, a key role in increasing the final consumer awareness towards its own energy consumption and consequently in bursting its active role in smart grids. The aim of this paper is to highlight the economic benefits of low cost intelligent control domestic devices, to identify energy behavior, system status and improve energy efficiency. The scope is to develop interaction between final users to create a network of energy consumption efficiency. The paper presents an application of Multi-scale Principal Component Analysis to diagnose inefficient occupant behavior and systems malfunctioning and suggest good practices of energy conservation.The reduction of the greenhouse gasses is a worldwide growing challenge. Looking at how the different sectors contribute to the total anthropogenic greenhouse gas (GHG) emissions, the contribution from residential and commercial buildings is 7.9 % of the total emissions [1]. These data reflect the fact that this sector is, by no means,one of the major contributors. Nevertheless, the sector is considered one of the most interesting when it comes to the estimated economical mitigation potentials with respect to GHG reduction in the 2030 forecast done by IEA. The challenge is to implement flexible, easy-to-install and easy-to-use solutions with built-in intelligence capable of i.e. timing the energy consumption with the availability of surplus energy from renewable energy sources. Development of smart sensors, advanced control systems and complex communications architectures for use in private households makes possible to have technology assistance to solve this challenge. The household sector has a wide and undeveloped potential basically due to economical causes. Energy efficiency initiatives lead to remarkable reduction of the energy bills and consequently in a short payback period for large users of both the tertiary and the industrial sectors compared to household sector. On the other hand, investments in energy
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