Search

Problem statement. The current direction of the development of the energy policy of Ukraine is the transformation of the residential communal complex into a system that makes it possible to implement organizational and technical methods for creating energy-efficient multi-apartment residential buildings. The advantage of designing energy-efficient buildings is a high level of comfort, creating a favorable microclimate in a residential building, reducing the negative impact on the environment, reducing harmful emissions into the atmosphere, and saving energy resources. One of the main characteristics of the energy efficiency of buildings is the specific energy consumption for heating and cooling the premises per year. In order to achieve a reduction in the specific rate of thermal energy consumption for heating and ventilation of the building, it is necessary to responsibly approach the issues of the efficiency of energy-saving systems. It is possible to achieve an increase in energy efficiency with the help of a combination of various constructive, engineering and architectural planning measures, as well as the simultaneous use of modern engineering energy-saving methods and technologies in combination with a compact form and layout of the building. The purpose of the article. Analyze the calculation of the cost of energy resources of an apartment building, taking into acount energy-efficient solutions. Conclusions. An analysis of the dependence of energy costs of a multi-apartment residential building on the level of implementation of energy-efficient systems was carried out. During the construction of residential complexes in Odesa, the use of the system of energy-efficient solutions and the “Smart House” control system allows you to significantly reduce energy costs. Due to the use of an automated control system for technical devices in the building, a reduction in transmission costs and costs for the functioning of the ventilation system is achieved., Постановка проблеми. Підвищення енергоефективності багатоквартирних житлових будинків – один із пріоритетних напрямків енергетичної політики України. Зниження енергозатрат у процесі експлуатації багатоквартирних багатоповерхових житлових будинків досягається комплексом організаційних і технічних заходів. Перевага проектування енергоефективних будинків полягає у високому рівні комфорту, створенні сприятливого мікроклімату у житловому будинку, зниженні негативного впливу на довкілля, зменшенні шкідливих викидів в атмосферу, економії енергоресурсів. Однією з основних характеристик енергетичної ефективності будівель прийнято вважати питоме енергоспоживання під час опалення та охолодження приміщень на рік. Мета статті – проаналізувати розрахунок вартості витрат на енергоресурси по рядовій секції індустріального будинку з урахуванням застосованих енергоефективних рішень. Висновки. Проведено аналіз залежності витрат на енергоресурси багатоквартирного житлового будинку від рівня впровадження енергоефективних систем. Суттєве зниження трансмісійних витрат та витрат на функціонування системи вентиляції можливе тільки за рахунок використання автоматизованої системи управління всіма технічними пристроями в будівлі. Впровадження системи енергоефективних рішень і системи регулювання «Розумний будинок» у м. Одеса у будівництво житлових комплексів дозволяє значно скоротити витрати на енергоресурси.

Incremental learning evolves deep neural network knowledge over time by learning continuously from new data instead of training a model just once with all data present before the training starts. However, in incremental learning, new samples are always streaming in whereby the model to be trained needs to continuously adapt to new samples. Images are considered to be high dimensional data and thus training deep neural networks on such data is very time-consuming. Fog computing is a paradigm that uses fog devices to carry out computation near data sources to reduce the computational load on the server. Fog computing allows democracy in deep learning by enabling intelligence at the fog devices, however, one of the main challenges is the high communication costs between fog devices and the centralized servers especially in incremental learning where data samples are continuously arriving and need to be transmitted to the server for training. While working with Convolutional Neural Networks (CNN), we demonstrate a novel data sampling algorithm that discards certain training images per class before training even starts which reduces the transmission cost from the fog device to the server and the model training time while maintaining model learning performance both for static and incremental learning. Results show that our proposed method can effectively perform data sampling regardless of the model architecture, dataset, and learning settings.

Electricity markets are becoming more integrated around the world. However, the knowledge of the effects of different auction formats on suppliers' strategies in the presence of transmission constraints and transmission costs is still very limited. In this paper, I analyze the performance of uniform and discriminatory price auctions in the presence of transmission constraints and transmission costs. When the transmission capacity is binding, the discriminatory price auction could outperform the uniform price auction, minimizing the equilibrium price and the transmission costs. Moreover, when the transmission capacity is binding, an increase in transmission costs could be pro-competitive when the auction is discriminatory, but not when the auction is uniform.

Capacity constraints on transmissions of electricity are raising an increasing policy concern as electricity markets are integrated around the world. But our understanding of the workings of such markets is still limited. The purpose of this paper is to highlight the impact of transmission capacity constraints and transmission costs on electricity market auctions. In the presence of transmission capacity constraints, the equilibrium is asymmetric even when the suppliers are symmetric in generation capacity and costs. An increase in transmission capacity induces non-monotonic changes in firms' profits. In the presence of transmission constraints and zero transmission costs, an increase in transmission capacity is pro-competitive; in contrast, then the transmission costs are positive, an increase in transmission capacity could be anti-competitive.

In this paper we analyze nonanticipative (delayless) transmission of source symbols with memory over channels with memory (with and without feedback). We employ duality of source, channel pairs with respect to distortion function, transmission cost pairs to show achievability of nonanticipative transmission in terms of excess distortion probability. We apply the method to the Binary Markov source with Hamming distortion function and the Binary Unit Memory channel with transmission cost, with the joint-design operating optimally and in real-time, with and without feedback encoding and decoding. © 2015 IEEE. 2015-June 521 525

We consider a unit memory channel, called Binary State Symmetric Channel (BSSC), in which the channel state is the modulo2 addition of the current channel input and the previous channel output. We derive closed form expressions for the capacity and corresponding channel input distribution for the BSSC with and without feedback and transmission cost. We also show that the capacity of the BSSC, with or without feedback, is achieved by a first order symmetric Markov process. © 2015 IEEE.