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Along the last years, electrical energy distribution companies have already done many investments in order to identify and calculate energy losses along a distribution network. Although these big efforts, most approaches for clearly identifying and solving the related problems still remain rather inefficient. The accurate identification and the precise calculation of electricity losses enables the clear specification of the critical points and segments in the networks and, consequently, the effective prioritization of actions and interventions in order to reduce those electricity losses and problems. Moreover, the work already performed on this issue, the existing approaches focus mainly on empirical and probabilistic data. Hence, there is still a clear gap between real information and the considered one, which tends to be poor and imprecise. Due to this reality and the lack of appropriate software applications, in this paper we propose a web platform for the management of the whole network of electrical energy distribution, from medium voltage (MV) down to low voltage (LV), including billing on the transformation centers (TCs), electricity losses calculation and proposals for solving actions, by means of a fuzzy decision-making model.

In this article we describe the step-by-step implementation of an agent that can trade the USD/JPY currency pair using a 6 hours timeframe. The agent is capable of trading autonomously due to its ability to handle money management and to decide when to buy or sell the currency pair. Its implementation consists of a prediction mechanism, which it uses to forecast the direction of the price, and a risk management system, which enables it to make decisions regarding how much to invest in each trade and when to avoid trading. We present several alternatives for the price prediction mechanism, from using a standalone classification or regression model to using an ensemble with fixed or dynamic vote weights. The agent performed simulated trades over a period of 17 months, and obtained a return of around 50% using low leverage and after taking into account the trading costs.