Abstract:A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.

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Due to the increase of the amount of electrical and electronical equipment waste (e-waste), the understanding of individual consumers’ main decision triggers represents a key point in increasing the quantity of recycled e-waste. A series of studies from the literature have shown a positive relationship between the consumers’ attitude, awareness, self-efficacy, social norms, and their e-waste recycling intention, as well as the positive influence between the intention and the manifested behavior. Additional to these determinants, in the present study, the influence of social media was analyzed along with the actions taken by the government and nongovernmental organizations, with the purpose to include and to capture, as much as possible, a high amount of determinants in the e-waste recycling process. Nevertheless, the demographic or socio-economic variables, such as age, gender, income, education, number of family members, etc., have shown over time to have a contribution to predicting the consumers’ pro-recycling behavior. As on one side, in the research literature, the opinions related to which of the demographic or socio-economic factors can have an impact on the recycling behavior have been divided and, on another side, a series of researchers believe that the discrepancies in the findings of different studies can be due to culture in various countries, in this paper we conducted such an analysis with reference to the Romania’s case. The results have shown that the demographic variables, such as age and gender, can have a contribution to predicting residents’ pro-e-waste recycling behavior. Based on these findings, the policymakers can gain a better understanding of the e-waste recycling phenomenon and on its main triggers, with results in creating better policies for sustaining a proper e-waste managing system.

In this paper demand side management (DSM), characterized by shifting techniques, is applied to a residential microgrid. It is supposed that the microgrid is managed by a prosumer, a decision maker who manages distributed energy sources, storage units, ICT elements, and loads involved in the grid. DSM is considered as an integral part of the optimal economic short-term management problem such that the allocation of shiftable loads is treated as a variable must be determined simultaneously with all the others variables (i.e. energy exchange with the main grid, internal production, charge/discharge of electrical storage units). This paper focuses on the formulation of an economic model including functional links between shiftable and shifted loads properly linked. The objective function is the minimization of the operation energy costs. The model is implemented using IBM ILOG CPLEX an optimization programming language solver. The analysis shows how the variable allocation of shiftable loads is related to the other variables and how all the variables are linked (directly or indirectly) to the energy price and to the other parameters typical of shiftable energy devices. Moreover, the model allows to easily perform sensitivity analyses by varying the parameters considered. For instance, transitioning from the pre-shift to post-shift state, an improvement of the economic objective corresponds to an enhancement in the utility load profile. A sensitivity analysis is carried out by varying the maximum amount of power exchanged with the main grid. Results provide useful information to find a compromise between connecting interests. Numerical results are presented and discussed

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The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand by loading local power grids, but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that exploits predictors compiled from Geographic Information Systems data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators that measure the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l 1 penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies. INDEX TERMS Electric vehicles, data analysis, charging infrastructure, spatial analysis, prediction methods, machine learning.

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This paper implements Data Envelopment Analysis (DEA) to calculate an efficiency measure index of 21 energy power plants that use different technologies, including both renewable and conventional types. Super-efficiency measurements are used to generate a ranking of plants based on their efficiency score that can be used to select among alternatives. It is also showed how DEA can also be adopted to estimate the amount of financial subsidies necessary to make a renewable energy plant as efficient as a conventional energy plant, by calculating the extent to which inefficient power plants over-utilize specific inputs or under-produce outputs.