This paper purposes to provide an user- friendly intelligent tool, integrating fuzzy controllers and multi-agent techniques, able to motivate and to support behavioral change of energy end- users, having as main objective to re-define the role of energy consumer in "prosumer" in the context of a reorganized decentralized energy market, now reported to intelligent grids (smart grids). Integration of interactive technologies in a decision support system for microgrids energy management optimizes: functioning from an economical point of view, active control of distributed generation, controlled consumption, loading the storage equipment. The added value of the proposed tool consists of integrating decision theory and artificial intelligence concepts in monitoring and control actions, allowing “prosumers”: to make energy usage data accessible and to demonstrate that energy savings can be achieved without compromising comfort levels.
The Scientific Buletin of Electrical Engineering Faculty, no.1, 2016, ISSN 2286-2455, DOI: 10.1515/SBEEF-2016-0011
Autori: Dragomir O.E., Dragomir F.
Proceedings of the 16th SGEM GeoConference on Energy and Clean Technologies, Albena, Bulgaria, 2016
Autori: Dragomir F., Dragomir O.E.
This article proposes an application for monitoring and diagnosis of ambient conditions, based on fuzzy- multi agent intelligent tools. The aim of this paper is to design, using Matlab, a graphical user interface able to assist the users for home smart energy savings. The main objectives of this approach is to minimize energy consumption, automate every-day tasks in smart settlements and increase energy efficiency, thought the integrated tools for monitoring and diagnosis. In this respect, firstly are presented concepts of: multi- agents and fuzzy logic controllers. Secondly, we have implemented these intelligent tools, using MATLAB programming language, in order to develop the prototype of a hybrid system for monitoring and diagnosis of ambient conditions (temperature, luminance, air condition, noise, amount of CO2 in air, humidity). The intelligent control techniques are tested using real monitored data and scenarios.
Proceedings of International Conference on Control, Decision and Information Technologies (CoDIT 2016), 6-8 April 2016, ISBN: 978-1-5090-2189-5 DOI: 10.1109/CoDIT.2016.7593558
Autori: Otilia Elena Dragomir, Florin Dragomir
The main idea of this paper is to develop an intelligent decision support system for decision support of green energy producers, anxious to participate to liberalized energy market. The international and national context is very favorable for the problematic discussed. It is represented by the new European Union strategies who promote: the green technologies and the methods for their integration into existing power systems. To valorize this context, we propose an intelligent decision support system that integrates a fuzzy logic controller and an expert systems and is able to give alternatives for action, through a specialized interface, to prosumers. The information provided by using advanced control algorithms will improve the information flow and will optimize the decisional process.
Journal of Applied and Physical Sciences, 2017, 3(1): 1-6
Autori: Florin Dragomir, Otilia Elena Dragomir, Mihaela Eugenia Ivan, Sergiu Stelian Iliescu, Ioana Stănescu
A Photovoltaic (PV) tracker system is one of those methods that are able to increase the PV power generation. Theoretically, a PV tracker system with two axes can increase the overall solar energy capture by about 45%, compared to a ϐixed PV module tilted at an angle equal to the local latitude. For one-axis tracking system, the increase is aproximately 32%. In this paper, we design and implement a twoaxis tracking system of PV systems that follows the Maximum Power Point (MPP) using a programmable circuit XILINX type Complex Programmable Logic Device- CPLD and Xilinx ISE software. Thus, PV module will reach its MPP in relation to date and time of the day. The test bed relies on an algorithm integrated in the XILINX that has as inputs: date, location’s latitude and longitude, the standard longitude (related to the location’s position in relation with Greenwich), and the number the positions of the Sun's path. To establish the position of the panel in a time of day value is determined by the following calculations: the angle of the day, correction factor of Earth's orbit, the solar declination angle, the equation of time in minutes, eastern time using latitude angle, the number of hours the Sun shines using angle eastern time, time the Sun sets, vectors containing the coordinates of the positions of the Sun (in this case 10 positions) during the day and azimuth angle.
Energies, Volume: 8, Issue: 11, Pages: 13047-13061, ISSN: 1996-1073, DOI: 10.3390
Autori: Dragomir O.E., Dragomir F., Stefan V., Minca E.
The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS) models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1) and the shape of membership functions (Scenario 2).
Studies in Informatics and Control- SIC, Volume 24, Issue: 3, ISSN: 1220- 1766, pp. 351-360, 2015
Autori: Dragomir O.E., Dragomir F., Stefan V., Minca E.
The goal of this paper is to propose a forecasting tool to producers/ consumers (prosumers) of renewable energy sources, based on artificial intelligence techniques, trying to obtain optimal predictions. The exploration and the assessment of the criteria used for choosing the adequate forecasting tool are made in the artificial intelligence context. In this respect, firstly, the criteria used for choosing the best forecasting technology, in relation to each step of the modelling process are presented. Secondly, the identified criteria are tested on two Adaptive Neuro- Fuzzy Inference System (ANFIS) models, in order to underline the effects of these users’ decisions over the forecasting performances.
Proceedings of the 19th International Conference on Control and Computing (ICSTCC), pp. 99 – 104, 14-16 Oct. 2015, Cheile Gradistei - Fundata Resort, Romania, 2015, ISBN: 978-147998481-7, DOI: 10.1109/ICSTCC.2015.7321276
Autori: Minca E., Coanda H.G, Dragomir F., Dragomir O.., Filipescu A
Proceedings of the International Conference on Mathematical Methods, Mathematical Models and Simulation in Science and Engineering. : New Developments in Pure and Applied Mathematics (MMSSE 2015), Vienna, Austria, March 15-17, pp. 343-348, 2015, ISBN: 978-1-61804-287-3, ISSN: 2227-4588, 2015
Autori: Dragomir O.E., Dragomir F., Minca E.
This paper purposes an intelligent decision support system for low voltage grids with distributed power generation from renewable energy sources (InDeSEn). The added value of this innovative software tool consists in integrating decision theory and artificial intelligence concepts in monitoring, supervising, forecasting and control actions, allowing prosumers of energy from renewable sources: to control electricity consumption of the used devices, to reduce their monthly bills, carbon emissions, energy demand during peak periods and to use more efficient the energy from renewable energy sources. The application is accessible to users anytime, through the web interface attached, providing both: information for general use and technical information.
The 2014 IEEE International Conference on Robotics and Biomimetics (RoBio2014), Bali, Indonesia, pp. 1904 – 1909, 2014, ISBN: 978-147997396-5, DOI: 10.1109/ROBIO.2014.7090614
Autori: Autori: Dragomir F., Dragomir O.E.
Advanced Materials Research, Volume 918, Chapter 3: Power, Energy and Environment Engineering, pp. 195-199, 2014, ISSN: 1662-8985, DOI:10.4028/www.scientific.net/AMR.918.195
Autori: Dragomir F., Dragomir O.E.
Renewable energy source (RES) enables us to diversify our energy supply. Renewable energy sources are getting more and more widespread, mainly due to the fact that they generate energy by keeping the environment clean. This increases our security of supply and improves European competitiveness creating new industries, jobs, economic growth and export opportunities, whilst also reducing our greenhouse gas emissions. This article proposes a simulation of a three-phase low voltage grid with power generation from photovoltaic sources. The proposed system consists of 192 photovoltaic (PV) panels distributed in 32 rows with each 12 PV panels.
Advanced Materials Research, Volume 918, Chapter 3: Power, Energy and Environment Engineering, pp. 200-205, 2014, ISSN: 1662-8985, DOI:10.4028/www.scientific.net/AMR.918.200
Autori: Florin Dragomir, Otilia Elena Dragomir
Recent studies suggest that in order to facilitate higher market and grid penetration of solar power, the users need accurate forecasts of generating power from photovoltaic (PV) plants on multiple time horizons. Despite the large number of forecasting methods, the comparison of results and evaluation of relative advantages between models has been evasive. The general purpose of the paper is to explore the way of performing accurate forecasts of generating power from renewable energy sources so that independent system operators can act consequently. Different aspects of radial basis functions (RBF) neural networks (NNs) are discussed and an illustration of the proposed predictor software interface is given.
Procedia Computer Science, Volume 31, Pages 474–479, 2014, ISSN: 1877-0509, DOI:10.1016/j.procs.2014.05.292
Autori: Dragomir O.E., Dragomir F., Radulescu M.
This paper proposes a Matlab object oriented application based on Kohonen Self- Organizing Maps (SOM) able to classify consumers’ daily load profile. Firstly, the characteristics of Kohonen self- organizing maps are briefly described in order to underline the advantages and disadvantages of these types of neural networks in classifications approaches. In the second part, data used for classification of load daily profiles is processed using statistical methods and Matlab. The result of these computations is a data base composed of daily load profiles used for SOM training. In the third part, the proposed software is tested on several scenarios in order to classify different consumers’ load profiles.
Proceedings of the International Conference on Automation, Quality and Testing, Robotics (AQTR 2014), Page(s): 1 – 6, 22-24 May 2014, Cluj-Napoca, Romania, Print ISBN: 978-1-4799-3731-8, DOI: 10.1109/AQTR.2014.6857913
Autori: Dragomir O., Dragomir F.
This paper proposes to prosumers a NN based decision support application for selecting an optimal forecasting tool for energy produced from renewable sources. The exploration and the assessment of criteria used for choosing a forecasting tool are made in the neural network (NN) framework. Firstly, the criteria for selecting the best forecasting tool are addressed. Secondly, the identified criteria are integrated in an object oriented software application, built using Matlab-Guide User Interface. In order to underline the effects of the users' decision making, in the third part, the forecasting performances of feed forward neural networks (FF-NN) are tested and evaluated.
The 14th International Multidisciplinary Scientific GeoConference & Expo SGEM2014, The 14th GeoConference on Energy and Clean Technologies, Section Renewable Energy Sources & Clean Technologies, Volume I, pp. 141-148, 17 - 26 June, 2014, Albena, Bulgaria, ISBN: 978-619-7105-15-5, ISSN: 1314-2704, DOI : 10.5593/sgem2014B41, DOI : 10.5593/SGEM2014/B41/S17.019
Autori: Dragomir O.E., Dragomir F.
Providing integrated solutions dedicated to optimizing management of microgrids with distributed power from renewable energy sources (RES), is an important contribution to promoting of clean energy technologies. The solutions involve, among others, the integration of artificial intelligence techniques able of: monitoring, assessing (diagnosis) and estimating (prediction) periods of overload or under load etc. and propose plan effective action in terms of: reducing costs, improving profits or reducing the microgrids vulnerability. Within this scope, a Matlab object oriented application based on adaptive neuro- fuzzy inference systems (ANFIS) was developed to facilitate forecasting energy production from RES. Firstly, the characteristics of ANFIS are briefly described in order to underline the advantages and disadvantages of these types of neuro- fuzzy systems in forecasting approaches. In the second part, are described the methodology and the flowchart used for GUI modeling, as well as, the data used for forecasting of produced energy from RES pre-processing using statistical methods and Matlab. The result of these computations is a data base used, in the third part, for ANFIS training and testing. The proposed graphical user interface (GUI) is tested in order to forecast de energy generation for short term. The effect of ANFIS parameters on the forecasting performances are underlined using root mead square error (RMSE).
The 14th International Multidisciplinary Scientific GeoConference & Expo SGEM2014, The 14th GeoConference on Energy and Clean Technologies, Section Renewable Energy Sources & Clean Technologies, Volume I, pp. 221-228, 17 - 26 June, 2014, Albena, Bulgaria, ISBN: 978-619-7105-15-5, ISSN: 1314-2704, DOI : 10.5593/sgem2014B41, DOI : 10.5593/SGEM2014/B41/S17.029
Autori: Dragomir F., Dragomir O.E., Arghira N., Calofir V.
This article proposes is a study on the performance of photovoltaic (PV) panels (SNMM135 from SUNEL) after 15 years of use. The proposed system consists of 11 photovoltaic (PV) panels conected in series, an invertor Sunny Boy 2100TL, a Sunny SensorBox with temperature sensor and irradiance sensor, and Sunny WebBox. The energy produced by the PV panels is taken directly from the inverter Sunny Boy 2100TL. Thus we have the voltage and current of the PV system. The temperature cell and radiation are obtained using Sunny SensorBox. Also, in this work we performed a simulation of I-V characteristic and P-V characteristic of PV system. This haracteristics are influenced by two factors, the solar radiation and the cell temperature. Simulation will be given the radiation and temperature cell values acquired. Thus, a comparison was made between the real system and simulation for current and active power variations curves.