Publications
Decision support system for a low voltage renewable energy system
Iulia Stamatescu, Nicoleta Arghira, Ioana Făgărăşan, Grigore Stamatescu, Sergiu Stelian Iliescu, Vasile Calofir
2017
Abstract
This paper presents the development of a decision support system (DSS) for a low-voltage grid with renewable energy sources (photovoltaic panels and wind turbine) which aims at achieving energy balance in a pilot microgrid with less energy consumed from the network. The DSS is based on a procedural decision algorithm that is applied on a pilot microgrid, with energy produced from renewable energy sources, but it can be easily generalized for any microgrid. To underline the benefits of the developed DSS two case scenarios (a household and an office building with different energy consumptions) were analyzed. The results and throw added value of the paper is the description of an implemented microgrid, the development and testing of the decision support system on real measured data. Experimental results have demonstrated the validity of the approach in rule-based decision switching.
Publication Details
Impact Factor: 3.252
Citations: 42
Deep learning techniques for load forecasting in large commercial buildings
Cristina Nichiforov, Grigore Stamatescu, Iulia Stamatescu, Vasile Calofir, Ioana Fagarasan, Sergiu Stelian Iliescu
2018
Abstract
As large scale energy management strategies have gradually shifted the focus from the producer to the consumer side, buildings are starting to play a critical role in the efficient management of the electrical grid. Moreover some buildings have become prosumers by integrating local generation capabilities from renewable sources thus inducing additional complexity into the operation of the energy systems. As alternative to conventional energy consumption modelling techniques, a blackbox input-output approach has the ability to capture underlying consumption patterns and trends while making use of the large quantities of data being generated and recorded through dense instrumentation of the buildings. The paper discusses and illustrates an approach to apply deep learning techniques, namely Recurrent Neural Networks implemented by means of Long Short-Term Memory layers, for load forecasting. We focus …
Publication Details
Citations: 39
Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach
Mircea Stefan Simoiu, Ioana Fagarasan, Stéphane Ploix, Vasile Calofir
2022
Abstract
Collective actions in the context provided by energy communities and more sobriety from energy users could both represent a potential solution for significantly reducing carbon emissions in residential areas. However, research are needed to properly understand, model and simulate the collective behaviour of communities owning a PV plant with energy sharing mechanisms suggested by an European directive. In this context, we propose a multi-agent modeling framework for simulating energy communities that is built upon a stochastic interpretation of the willingness of energy users to modify their consumption. The proposed concept includes an intelligent decision support system that assists community members during their daily activities and provides optimal recommendations to minimise the collective net-energy-exchanged-with-the-grid. The paper includes a case study where we present the impact of …
Publication Details
Impact Factor: 8.7
Citations: 20
Optimising the self-consumption and self-sufficiency: A novel approach for adequately sizing a photovoltaic plant with application to a metropolitan station
Mircea Stefan Simoiu, Ioana Fagarasan, Stéphane Ploix, Vasile Calofir
2021
Abstract
The recent trends in designing sustainable power systems emphasise the importance of self-consumption (SC) both at individual and community level. This new paradigm changes the way in which we design photovoltaic facilities for residential houses and for various municipality services as well.In this context, the paper aims to formulate several optimisation problems using criteria such as self-consumption, self-sufficiency (SS) and net present value (NPV) as objectives to provide an optimal photovoltaic (PV) plant size for a singular power system - a subway station. By using this multi-objective approach, the work emphasises how each criteria impacts the profitability and value of the overall investment, involving possible shareholders in the design process by choosing a desired solution from the Pareto-efficient set of configurations. Moreover, a global optimal solution is provided by formulating an optimisation …
Publication Details
Impact Factor: 11.1
Citations: 20
Advanced control strategies for irrigation systems
Tiberiu Marinescu, Nicoleta Arghira, Daniela Hossu, Ioana Fagarasan, Iulia Stamatescu, Grigore Stamatescu, Vasile Calofir, Sergiu Iliescu
2017
Abstract
Water management and irrigation scheduling have become the main subjects of different studies in the last decades, due to their high influence on crop performance indicators. This study presents the most important parameters that have to be monitored in an irrigation management system and the most important ones are synthesized: air moisture and temperature, soil air and moisture, evapotranspiration. Based on the monitoring of these parameters, different control strategies and methods can be applied for optimization and efficiency of irrigation systems. The synthesis in this paper starts with classical control systems and, also, advanced methods such as fuzzy concept, decision support systems and model predictive control. Considering the currently necessity of integration into the Cyber-Physical Systems (CPS) concept, the paper finally proposes an irrigation control system for vineyards. The SCADA …