Abstract
Because of various developments in communications and technologies, each residential consumer has been enabled to contribute in Demand Response Programs (DRPs), manage its electrical usage and reduce its cost by using a Household Energy Management (HEM) system. An operational HEM model is investigated to find the minimum consumer's cost in every DRP and to guarantee the end-user's satisfaction, as well as to ensure the practical constraints of every battery and residential appliance. The numerical studies show that the presented method considerably affects the operational patterns of the HEM system in each DRP. According to the obtained results, by employing the presented method the consumer's cost is decreased up to 40%. © 2017 IEEE.

Abstract
This paper presents a new proposal for sensor fusion in power system state estimation, analyzing the case of data sets composed of conventional measurements and phasor measurements from PMUs. The approach is based on multiple criteria decision-making concepts. The equivalence of an L-1 metric in the attribute space to the results from a Bar-Shalom-Campo fusion model is established. The paper shows that the new fusion proposal allows understanding the consequences of attributing different levels of confidence or trust to both systems. A case study provides insight into the new model.

Abstract
Distribution automation systems in terms of automatic and remote-controlled sectionalizing switches allows distribution utilities to implement flexible control of distribution networks, which is a successful strategy to enhance efficiency, reliability, and quality of service. The sectionalizing switches play a significant role in an automated distribution network, hence optimizing the allocation of switches can improve the quality of supply and reliability indices. This paper presents a mixed-integer nonlinear programming aiming to model the optimal placement of manual and automatic sectionalizing switches and protective devices in distribution networks. A value-based reliability optimization formulation is derived from the proposed model to take into consideration customer interruption cost and related costs of sectionalizing switches and protective devices. A probability distribution cost model is developed based on a cascade correlation neural network to have a more accurate reliability assessment. To ensure the effectiveness of the proposed formulation both technical and economic constraints are considered. Furthermore, introducing distributed generation into distribution networks is also considered subject to the island operation of DG units. The performance of the proposed approach is assessed and illustrated by studying on the bus 4 of the RBTS standard test system. The simulation results verify the capability and accuracy of the proposed approach.

Abstract
Research management promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of a higher education institutions' research information assets. These assets may include databases, documents, policies and procedures. Conceptually linked, knowledge and research assume critical relevance as an essential tool to insuring Higher Education institutions quality. Institutions are challenged to develop robust (internal) quality assurance systems in which information about scientific production, research projects, staff curricula are considered as relevant indicators. This commitment with science and research is also visible by the opportunities promoted by institutions for the academic development of their staff. Accordingly, the assessment of research and science indicators becomes an essential step for the definition of research development programmes in HE institutions. Based on this framework, it was developed an online questionnaire to be answered by academic staff, trying to assess some science and research indicators. Trying to measure the research potential of all faculty staff, this assessment tool is organized in distinctive four dimensions, namely researcher's (i) biographic data, (ii) scientific identification, scientific outputs (books, Books' chapters, scientific paper indexed and proceedings), (iii) research project with competitive funding and (iv) suggestions to improve research production. In what concerns to the application, all faculty staff members (teachers and researchers) were invited to contribute. The results were presented and discussed personally and collectively with all academic community. These results also provide relevant Key Performance Indicators, also known as KPIs or Key Success Indicators (KSIs), that could help managers and researchers gauge the effectiveness of various functions and processes important to achieving organizational goals. If scientific research is a strategic priority to higher education institutions, this kind of KPIs could be used to help academic managers to assess whether they or their faculty/research staff are on or off target towards those goals.

Abstract
The liquefied natural gas (LNG) trade provides the means of trading gas globally and represents about 10% of the gas trade. The forecasts show the LNG business will grow, over the next 20 years, at about twice the rate of the whole gas trade. Although the current state of LNG trade is well studied, the literature on the future business structure of it is limited and conflictual. This work considers the future LNG business structure by comparing the development trajectories of the oil and LNG sectors. In addition, it assesses the conclusions drawn by researchers against this background and the current pattern of change in the industry. The comparison involves three stages: (1) trade flows-oil and LNG trade flows are very similar, mainly due to the common distribution of the oil and gas reserves. (2) Supply chain configuration-the international trade for both fuels is tanker based thus allowing for a similar market responsive trade policy, i.e., real-time destination selection (spot sale) at a global scale. (3) Institutional developments-the current transparent and competitive global oil trade, with prices dominated by physical and paper markets, was driven previously by long-term contracts, in the same manner as the current LNG business. This analysis, together with transaction cost economics, supports the argument that, in future, LNG spot trade will increase and give rise to a competitive and globally unified LNG market. Further-more, LNG pricing will become transparent and would be dominated by physical and paper markets benchmark prices. (C) 2016 John Wiley & Sons, Ltd.

Abstract
Time series forecasting is a challenging task, where the non-stationary characteristics of data portray a hard setting for predictive tasks. A common issue is the imbalanced distribution of the target variable, where some values are very important to the user but severely under-represented. Standard prediction tools focus on the average behaviour of the data. However, the objective is the opposite in many forecasting tasks involving time series: predicting rare values. A common solution to forecasting tasks with imbalanced data is the use of resampling strategies, which operate on the learning data by changing its distribution in favour of a given bias. The objective of this paper is to provide solutions capable of significantly improving the predictive accuracy on rare cases in forecasting tasks using imbalanced time series data. We extend the application of resampling strategies to the time series context and introduce the concept of temporal and relevance bias in the case selection process of such strategies, presenting new proposals. We evaluate the results of standard forecasting tools and the use of resampling strategies, with and without bias over 24 time series data sets from six different sources. Results show a significant increase in predictive accuracy on rare cases associated with using resampling strategies, and the use of biased strategies further increases accuracy over non-biased strategies. © 2017, Springer International Publishing Switzerland.