Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce ChangeAdvisor, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44,683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of ChangeAdvisor in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that ChangeAdvisor is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%)
Renewable energy sources (RES) are mainly used in the electrical sector. Electricity is not a storable commodity. Hence it is necessary to produce the requested quantity and distribute it through the system in such a way as to ensure that electricity supply and demand are always evenly balanced. This constraint is actually the main problem related to the penetration of new renewables (wind and photovoltaic power) in the context of complex energy systems. Moreover the design of optimal energy resource mixes in climate change mitigation actions is a challenge faced in many places.The paper analyzes the problem of new renewable energy sources penetration. The case of Italian scenario is considered as a meaningful reference due to the characteristic size and the complexity of the same.The various energy scenarios are evaluated with the aid of a multipurpose software taking into account the interconnections between the different energetic uses. In particular it is shown how the penetration of new renewable energy sources is limited at an upper level by technological considerations and it will be more sustainable if an integration of the various energy uses (thermal, mobility and electrical) will be considered. A series of optimized scenarios are developed. In each case the maximum RES penetration feasible with the constraints was defined. Then analysis is applied to an energy system model of Italy showing how an integrated development of CHP and electric mobility can aid a further integration of wind and photovoltaic energy power. Finally the primary energy consumption saving possible in case of consistent penetration of intermittent renewables and CHP was identified
Nowadays, mini-grids can provide reliable and cheap electricity also to far communities of developing countries. Diesel generators can ensure backup power, in addition to renewable sources and energy storage devices. However, poor infrastructures, severe weather conditions and a difficult procurement chain can strongly influence the fuel delivery, thus reducing the continuity of supply. The present paper proposes a stochastic method to optimize the design of a rural mini-grid composed by a photovoltaic plant, a lithium battery, a diesel generator and a fuel tank. The fuel procurement strategy and its mathematical model are also discussed and simulated. A Particle Swarm Optimization (PSO) procedure is applied to the optimal sizing of components, in combination with a Monte Carlo technique aimed to handle the uncertainties of fuel delivery, irradiance and load. A case study for a possible mini-grid in Uganda is discussed, also performing a sensitivity analysis of the results with respect to the fuel delivery time, the fuel price and the cost of load curtailment
Hybrid mini-grids are promising solutions to foster the universal electricity access in developing countries. While renewable sources, possibly combined with energy storage devices, help in reducing the environmental impact and the operational costs of electricity supply, a backup diesel generator can increase the continuity of service when RES are not available or very discontinuous. The fuel procurement can be a serious issue in rural areas, due to lack of good infrastructures, combined to long distances existing between the mini-grid and the fuel station; however, this aspect is usually disregarded in designing the mini-grid. Moreover, the traditional sizing of rural mini-grids is based on simulating simple operational strategies. Rolling horizon strategies can be more efficient since the system is redispatched also infradaily, thus leading to possible reductions of operational costs and load curtailment. The present paper proposes a novel probabilistic technique for the optimal sizing of a mini-grid, considering both the fuel procurement issues and a short-term rolling-horizon scheduling of resources. This method is applied to a system composed by a photovoltaic plant, a lithium battery, a diesel generator, and a fuel tank, minimizing the net present cost of the system over the project lifetime. A numerical case study is discussed
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