Study on electricity markets in Romania

In this paper, we detail about the components of the wholesale electricity market in Romania: Market for Bilateral Contracts (Central Market with continuous double negotiation of bilateral electric energy contracts (CM - OTC), Centralized Market for bilateral electric energy contracts), Day-Ahead Market (DAM), Inter-Daily Market (IM), Balancing Market (BM), Centralized Market for universal service (CMUS). In addition, for each type of market we generated diagrams with the main business processes

Trustful Blockchain-Based Framework for Privacy Enabling Voting in a University

In this study, we explore the challenges and potential solutions to blockchain-based voting. As a first step, we present a comparison of the relevant platforms for implementing smart contracts in decentralized applications (dApps). We analyze the top platforms, highlighting their advantages and disadvantages, their architecture, and which are more reliable for developing smart contracts. The goal is to find a technology that offers various facilities to the developer and multiple functionalities and performance in the development of smart contracts in a field that has seen an incredible pace of innovation. Based on the findings from our research, we propose a framework based on blockchain technology and smart contracts for university-level voting based on blockchains

Exploring Data in Human Resources Big Data

Nowadays, social networks and informatics technologies and infrastructures are constantly developing and affect each other. In this context, the HR recruitment process became complex and many multinational organizations have encountered selection issues. The objective of the paper is to develop a prototype system for assisting the selection of candidates for an intelligent management of human resources. Such a system can be a starting point for the efficient organization of semi-structured and unstructured data on recruitment activities. The article extends the research presented at the 14th International Conference on Informatics in Economy (IE 2015) in the scientific paper "Big Data challenges for human resources management"

PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context

Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much„ energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to operate unattended. By adding the information collected by sensors managed with powerful technologies such as big data and analytics, the system is able to efficiently react to environmental factors and respond to consumers’ requirements in real time. According to the weather parameters, the output of PV could be symmetric, supplying an asymmetric electricity demand. Thus, a smart adaptive switching module that includes a forecasting component is proposed to improve the symmetry between the PV output and daily load curve. A scaling approach for smaller off-grid systems that provides an accurate forecast of the PV output based on data collected from sensors is developed. The proposed methodology is based on sensor implementation in RES operation and big data technologies are considered for data processing and analytics. In this respect, we analyze data captured from loggers and forecast the PV output with Support Vector Machine (SVM) and linear regression, finding that Root Mean Square Error (RMSE) for prediction is considerably improved when using more parameters in the machine learning process

Internet of Things, Challenges for Demand Side Management

The adoption of any new product means also the apparition of new issues and challenges, and this is especially true when we talk about a mass adoption. The advent of Internet of Things (IoT) devices will be, in the authors of this paper opinion, the largest and the fastest product adoption yet to be seen, as several early sources were predicting a volume of 50 billion IoT devices to be active by 2020 [1][2]. While later forecasts reduced the predicted amount to about 20-30 billion devices [3], even for such “reduced” number, demand side management issues are foreseeable, for the potential economic impact of IoT applications in 2025 will be between 3.9 and $11.1 trillion USD [4]. Not only that new patterns will emerge in energy consumption and Internet traffic, but we predict that the sheer amount of data produced by this quantity of IoT devices will give birth to a new sort of demand side management, the demand side management of IoT data. How will this work is yet to be seen but, at the current moment, one can at least identify the bits and pieces that will constitute it. This paper is intended to serve as short guide regarding the possible challenges raised by the adoption of IoT devices. The data types and structures, lifecycle and patterns will be briefly discussed throughout the following article

Development of Spatial Database for Regional Development in Romania

eographical Information Systems are used in solving many regional development related problems, around the world. Starting from some national programs to famous international ones, such as INSPIRE program, each such initiative uses geospatial data as well in the process of building regional development strategies. This paper presents the main technical components of a geographical information system, meaning the spatial database, the web mapping server and the APIs used to embed the maps into web applications. The development steps for a pre-alpha version of a web GIS application dedicated to the regional development in Romania are also shown. The software tools which were integrated in order to develop the online application were Oracle Spatial, where geospatial data was stored, GeoServer, an open source web mapping server used to generate the map out of the data from Oracle Spatial’s tables and ASP.NET as a web framework for building the website