Defects, dopants and Li-ion diffusion in Li2SiO3

Запропонована логіко-структурна схема концепції управління інвестиційним забезпеченням промислового підприємства, яка враховує положення підприємства в зовнішньому та внутрішньому середовищах та підвищення ефективності його функціонування. Використання комплексного підходу щодо оцінки рівня інвестиційного забезпечення промислового підприємства дає можливість визначити позицію, яку воно посідає на конкурентному ринку і, відтак, сформувати необхідну для потенційного інвестора уяву про підприємство

Renewable Energy Sources and Impact on GDP Growth

Iinvestments in renewable energy sources (RES) appear to boost GDP growth especially in middle-income countries. By investing in RES, countries meet commitments for Net-Zero by 2050 and accelerate growth in ways that produce broader benefits to an economy. In Greece, the primary energy from RES doubled during the decade 2006-2016 thus contributing to a growing share of RES in the production of electricity. RES' contribution tripled as a percentage of the total electricity produced. Using statistical tools, the relation of RES to GDP during this period points to positive associations between RES and important macro-economic variables and reveals easurable impact on overall GDP growth. © 2021 IEEE

Dynamic data driven partitioning of smart grid using learning methods

A plethora of energy management opportunities has emerged for electricity consumers and producers by way of the transition from the current grid infrastructure to a smart grid. The aim of this chapter is to present a new dynamic data-driven applications systems (DDDAS) methodology for partitioning the smart distribution grid based on dynamically varying data. In particular, the proposed methodology uses the k-means algorithm for performing partitioning and a fuzzy decision making method for increasing power efficiency and reliability. The network is divided into a set of "similar" subnetworks; where the subnetworks are comprised of residential customers (i.e., residencies) who share the same characteristics pertaining to the energy needs but not necessarily the same geographic vicinity or belong to the same grid node. A fuzzy logic method is used to make decisions on which partitions could be offered energy at lower prices available from Renewable Energy Sources (RES). Various scenarios based on the GridLAB-D simulation platform exhibits how the operation of the smart grid is affected from the partition of the distribution grid. The illustrative example utilizes the IEEE-13, IEEE-37 and IEEE-123 bus test feeders in the experiments from a distribution grid composing 3004 residencies and both conventional and distributed generators. © Springer Nature Switzerland AG 2018. All rights are reserved

A meta-modeling power consumption forecasting approach combining client similarity and causality

Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and smart energy pricing. The data collection process for client consumption models is not always ideal and the resulting datasets often lead to compromises in the implementation of forecasting models, as well as suboptimal performance, due to several challenges. Therefore, combinations of elements that highlight relationships between clients need to be investigated in order to achieve more accurate consumption predictions. In this study, we exploited the combined effects of client similarity and causality, and developed a power consumption forecasting model that utilizes ensembles of long short-term memory (LSTM) networks. Our novel approach enables the derivation of different representations of the predicted consumption based on feature sets influenced by similarity and causality metrics. The resulting representations were used to train a meta-model, based on a multi-layer perceptron (MLP), in order to combine the results of the LSTM ensembles optimally. This combinatorial approach achieved better overall performance and yielded lower mean absolute percentage error when compared to the standalone LSTM ensembles that do not include similarity and causality. Additional experiments indicated that the combination of similarity and causality resulted in more performant models when compared to implementations utilizing only one element on the same model structure. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Enhanced short-term load forecasting using artificial neural networks

The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Comparative Energy Information Analytics of Five European Economies

Real GDP per capita and energy consumption are often, but not always, correlated variables. Comparative analysis of their respective time series offers new insights and information and new decision-making metrics. During the Great Recession, Greece faced a sudden and dramatic decline in GDP and a prolonged debt crisis fueled by structural issues and severe austerity measures. While all other European economies rebound and experienced GDP growth Greece continued to decline. Energy consumption, however, declined across Europe regardless of GDP growth. Using comparative information analytics, trends are identified, underlying factors unfolded and conclusions are drawn on the energy behavior of Greece's economy as well as those of Germany, Italy, France and Bulgaria. © 2020 IEEE