Summary This paper presents a modified whale optimization (MWO) algorithm for solving the problem of parameters estimation for input‐output curves of thermal and hydro generating units that operates in power plants. In order to improve the MWO algorithm, a chaotic component based on logistic map is added. This new algorithm is called chaotic MWO (CMWO). Both MWO and CMWO algorithms have kept the structure of original whale optimization (WO) algorithm, but some characteristics regarding how the solutions positions are updated inside CMWO and MWO phases were modified. Estimation of curve parameters is an optimization problem. Its objective is to minimize the sum of absolute errors between actual values and estimated values. The efficiency of CMWO, MWO, and WO algorithms is tested either on polynomial models or on models that use nonsmooth functions. The results of CMWO algorithm are then compared with the ones obtained by other recently published algorithms.

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Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.

The paper represents a reaction to the alarm signal made by the physician of Oradea's Power Distribution Company (S.D. Oradea). The physician noticed above the average values tendency to illness, by the operating personnel from the electric stations. The first part contains some considerations on the risk concept. References are made to risk factors, evaluation of damage level, and risk management in power companies. The second part presents the results of the study made in S.D. Oradea, regarding the electromagnetic pollution in high and medium voltage stations. There are presented maximum values of electric field intensity distribution and the induction of magnetic field; references are made on the risk's influence on the electric station operators.

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A variety of strategies intended to support environmentally friendly and resource-efficient building processes comprise sustainable construction policies. The limited number of bibliometric analyses in the field may hinder the ability to assess the efficacy and impact of research efforts, impede the potential for collaboration, and even limit the dissemination of best practices. Therefore, the present study aims to analyze the impact of published data on the topic of energy efficiency of buildings using the Web of Science core collection database. We perform a bibliometric analysis and science mapping research that assesses significant parameters for the field. A total of 28,555 papers were analyzed using the VOSviewer program. The data was divided into two periods to determine the evolution of trends in this field. The most prolific countries in this field were China, the United States, and England. Following the analysis of the collaboration maps, it was determined that there is a strong collaborative relationship between these countries in the development of papers. The most prolific papers of the first period were published in Energy Policy and Energy and Buildings, which also ranked first in the second period, followed by Energies. It was observed that the most frequent terms used in literature searches in the field differ according to the periods analyzed. In the beginning, the most frequent term was “energy efficiency and performance”, and between 2011 and 2023, the terms “applied energy” and “renewable and sustainable energy” increased considerably with technological development. The results of this research demonstrate the significant and expanding scientific interest in this area and serve as a valuable asset for researchers studying the energy efficiency of buildings.

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The energy efficiency of a system and the performance level of its equipment and installations are the two key elements based on which the investment decision in its modernization is made. They are also very important for setting up optimal operation strategies. The energy audit is a well-known and worldwide recognized tool for calculating energy performance indicators and developing improvement measures. This paper is a synthesis of the energy audit results performed for a district heating network that uses geothermal energy as its primary source of energy. The location of the heating system is inside a university campus. The first part explains the necessity of a comprehensive study on district heating networks and introduces the defining elements that characterize the analyzed equipment and installations. The complex energy balance methodology that has been developed and applied to this district heating system is presented in the second part of the paper. Next, the methodology for collecting the input data for the energy and mass balance is explained. In the final part, the numerical values of the performance indicators and the technical measures that must be applied to improve energy efficiency are shown, and conclusions are drawn.