• Energy Research

Industrial objects constructed between the XIX century and the first half of the XX century were scattered outside the historical city center. However, they are currently located within the boundaries of the historical area of a big city. Postindustrial objects that have lost their initial function create opportunities for city development. An urgent problem of urban planning in Lviv is to determine the prospects for the development and reconstruction of industrial areas, complexes, and buildings. The purpose of the work is to identify the modern urban trends in the development process of postindustrial areas located in the historic area of Lviv, as well as its compliance with the city development strategy. The article investigates the connection of new objects with the main functions of the district, provides a historical excursion of their development, on-site studies, and a comparative analysis of objects with the goals of the master plan of Lviv and of the integrated concept of development of the central part of the city. The studied objects of the urban revitalization of postindustrial buildings and areas are grouped according to new dominant function: 1—Development of the creative industry; 2—Revalued multifunctional areas; 3—Implementation of the concept of city center expansion; 4—Reconstruction of historical localities, and 5—Creation of new residential complexes. The authors consider the process of re-urbanization in some particular postindustrial areas and objects as a search for ideas to generalize their use for the whole city. The intensive industrial development of the city during the Soviet period led to strict regulation of other areas. After obtaining independence, there is an opportunity to fix the imbalance. This process occurs in different ways. In particular, the “in situ” review provides an opportunity to understand the development of the territory—whether it follows the master plan (or contradicts it), whether it meets the new development strategy of the city, whether it meets the needs of the local community, or meets the interests of developers only.

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.

With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.

Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.

Increasing the energy efficiency of production processes is closely related to minimizing the impact on the environment and is one of the priorities of the concept of sustainable development. Electric discharge is an effective tool for multilevel grinding of non-metallic materials in various working fluids and obtaining coarse and fine suspensions. We introduce the technique for calculating the electrotechnological parameters necessary for energy-efficient electric discharge dispersion. This technique considers the strength characteristics of the crushed material (dispersed phase) and the electrical conductivity of the working fluid (dispersed medium). It is also essential to consider the energy stored in the capacitor bank, the energy criterion, the critical value of the working fluid’s electrical strength, the radius of the high-voltage electrode point, and the distance from the discharge channel axis to the disintegration object. All this allows obtaining a given granulometric composition of the dispersed phase with minimal energy consumption. Experiments confirmed the validity of the proposed calculation technique. We obtained the water-brown coal suspension with a given dispersion two times faster and consumed four times less energy in comparison with the known methods that did not take into account the electrical conductivity of the working liquid and the mechanical strength of the crushed material.

The results of the calculation of the energy band structure and luminescent research of CeF3 crystals are presented. The existence of two 5d1 and 5d2 subbands of the conduction band genetically derived from 5d states of Ce3+ ions with different effective electron masses of 4.9 me and 0.9 me, respectively, is revealed. The large electron effective mass in the 5d1 subband facilitates the localization of electronic excitations forming the 4f-5d cerium Frenkel self-trapped excitons responsible for the CeF3 luminescence. The structure of the excitation spectra of the exciton luminescence peaked at 290 nm, and the defect luminescence at 340 nm confirms the aforementioned calculated features of the conduction band of CeF3 crystals. The peculiarities of the excitation spectra of the luminescence of CaF2:Ce crystals dependent on the cerium concentration are considered with respect to the phase formation possibility of CeF3.