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The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The ''zero energy buildings'' idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of CO2 into the environment, is now one of the most significant initiatives connected to building energy efficiency. This article describes a computational intelligence method to detect anomalous variations in a facility's energy use and infer a potential cause of such changes. The model is built using five sets of historical power consumption data from three buildings spread across four nations (Ecuador, Spain, France, and Canada), which are categorized based on the anomaly type each piece of data represents. Through a statistical study of the confidence interval, the proposed method, first determines the consumption patterns for each day of the week in each of the building's data sets. After normalizing the day to be studied toward its ''Z'' value, it is then cataloged using a machine learning model. The proposed method is evaluated in comparison to a purely statistical method called SAEEC methodology and it is discovered that the proposed method offers a relative improvement in accuracy, false positive rate (FPR), and false negative rate (FNR) of 12.41%, 42, 36%, and 42.45%, respectively, for the detection of atypical values in electrical energy consumption.

During the summer season in subtropical countries and the dry season in tropical countries, air temperature is high. To adapt this condition, an air conditioning (AC) unit is used. The air conditioning use, however, increases the electrical energy consumption which contributes to the increase of indirect CO2 emissions. For many buildings, a monthly electrical energy cost from the use of AC system can contribute up to 40% of a monthly utility expenditure. It is then a great motivation to decrease electrical energy consumption of the AC unit. In this work, an adaptable preventive maintenance for an AC unit shows an acceptable level of electrical energy decrease. An experiment was performed for 2 AC types. The first one is a wall mounted unit with a cooling capacity of 9,300 BTUH. The second unit is a split duct unit with a cooling capacity of 48,000 BTUH. The cleaning of evaporator in the wall-mounted AC unit decreased the hourly electrical energy consumption by 4.1% from 810 to 777 Watt-hour. In addition, cleaning of condenser for the similar AC unit decreased the electrical energy consumption by 6.2% from 810 to 760 Watt-hour. Meanwhile, the evaporator cleaning for a 48,000 split duct AC decreased the hourly electrical energy consumption by 2.4% from 4.64 to 4.53 kWh and cleaning of condenser decreased the energy consumption by 5.4% from 4.64 to 4.39 kWh. If this electrical energy decrease could be scaled up to the global energy consumption from the air conditioning use, this should be a significant decrease of the global energy consumption and the correlated indirect CO2 emissions from the air conditioning sector. [ABSTRACT FROM AUTHOR]

Textile industries are water intensive and the effluent generated contains salts, dyes, and heavy metals as well as having a high biological oxygen demand (BOD) and chemical oxygen demand (COD), therefore it must be treated before being discharged into the aquatic environment to prevent its contamination. Electrocoagulation (EC) is a promising technique to treat domestic and industrial effluent, where in situ coagulants are produced by sacrificial dissolution of the anode by application of an electric current across the electrodes. The effect of punched-hole electrodes on the performance of EC—color removal efficiency (CRE), electrical energy consumption (EEC), and operating cost (OC)—was studied by varying the number and diameter of holes in the electrode. It was observed that the use of punched electrodes resulted in higher CRE, lower EEC, and lower OC as compared with plane electrodes. The difference in CRE obtained using plane and punched electrodes was found to increase with an increase in current. The effect of punched-hole electrodes on the treatment of real dye effluent was studied and COD removal of 78.79% was obtained using punched-hole electrodes as compared with 69.30% using plane electrodes. The EEC and the OC were found to be 0.5 kW·h/m3 and US$0.424/m3, respectively, for punched-hole electrodes. [ABSTRACT FROM AUTHOR]

As a critical digital infrastructure, computing power has become the core productivity and a new engine driving economic growth in the digital economy. Nevertheless, the power-hungry nature of computing/data centers, representing the computing infrastructure, consumes a significant amount of electrical energy. Currently, China's economy is transitioning from high-speed growth to high-quality development. It is imperative to study how to coordinate the development of computing power while ensuring its safety and achieving green and low-carbon goals. Based on an overview of the current status of computing power development, this study predicts the future demand for computing power in China, analyzes the relationship between future computing power growth and electricity consumption, and discusses the associated challenges. From the perspectives of top-level design, regional layout, platform construction, and market mechanisms, this study proposes strategies and measures to accelerate the green and low-carbon transformation of computing power, providing support for sustainable computing power transformation and empowering the highquality development of the digital economy. [ABSTRACT FROM AUTHOR]

Kedungsepur is a national urban area located in the northern region of Java Island, comprising of six autonomous regions: Semarang City as the principal urban center, along with Kendal Regency, Semarang Regency, Salatiga City, Grobogan Regency, and Demak Regency as supporting areas. Kedungsepur holds significant economic importance in Central Java, contributing approximately 24% to the total Gross Regional Domestic Product (GRDP) over the past two decades. To strengthen Kedungsepur 's role in the regional economy, the government has invested in infrastructure, aiming to bolster the overall economy in Central Java, particularly in the northern region. Therefore, it is important to assess the economic impact of providing infrastructure in the Kedungsepur area to determine the extent to which infrastructure contributes to the regional economy. To measure the economic impact of infrastructure in Kedungsepur, this research uses a regression model with panel data and uses GRDP data as the dependent variable, and infrastructure utilization, especially road utilization and electricity consumption as independent variables. To ensure that regional economic growth in Kedungsepur is influenced by the existence of infrastructure, this research also uses household consumption data as a control variable. The research results show that regional economic growth in Kedungsepur is not only influenced by infrastructure utilization, but also household consumption. Among these, road usage emerges as the primary contributor to the economic growth of the Kedungsepur region, surpassing the impact of electricity consumption and household spending

Abstract. The idea of designing a controller for electrical energy management is due to the need for energy saving and intelligent management of the consumed energy. The article presents the designed and studied device designed to limit the consumption of electrical energy within one day and the magnitude of the load current in single-phase circuits. Thus, the consumer (household consumer) of electrical energy can use the energy at time and in amount at will, but within pre-set limits. The paper discusses the principle of operation of the device for power consumption management, its main characteristics, and applications.

This study contains a survey on the architecture of electric vehicles and an artificial neural network application for prediction of energy consumption in all-electric vehicles. In this study, the term “electric vehicles” (EVs) refers to various types of electrified vehicles. The technologies behind these electric vehicles were also discussed. The survey focuses on hybrid electric vehicles (HEVs), pure electric vehicles (PEVs), and plug-in hybrid electric vehicles (PHEVs). The study also presents the design simulation of a typical hybrid electric vehicle. A hybrid electric vehicle was designed using ADVISOR, and it was compared with another car known as the targeted car. The fuel consumption of the designed car was found to be lower than that of the targeted car. The study also introduced a multifunctional artificial neural network model for predicting electrical energy consumption in all-electric vehicles. The proposed model has nine input variables, which are virtual functions calculated from the nine selected parameters using a virtual function formula. The number of input variables was made to be equal to the number of output variables so that the artificial neural network could simulate a unique solution. The proposed model was compared with a multi-output inverse function model of an artificial neural network. The accuracy of the proposed model was 1.23–6.85 times higher than that of the inverse function model for the nine case studies considered in terms of mean square error.

The idea of designing a controller for electrical energy management is due to the need for energy saving and intelligent management of the consumed energy. The article presents the designed and studied device designed to limit the consumption of electrical energy within one day and the magnitude of the load current in single-phase circuits. Thus, the consumer (household consumer) of electrical energy can use the energy at time and in amount at will, but within pre-set limits. The paper discusses the principle of operation of the device for power consumption management, its main characteristics, and applications. [ABSTRACT FROM AUTHOR]

Building energy efficiency has grown strong in a context of soaring energy prices, especially in Europe. The use of energy-saving devices strongly influences its improvement, but in many cases, it is far from sufficient., especially if the energy comes from renewable sources with forced production. In the case of buildings, these are usually photovoltaic (PV) sources. For this reason, energy management systems (EMS) are becoming increasingly popular as they allow the increase in self-consumption and reduce the size of energy storage. This article presents analyses of historical energy consumption profiles in selected small- and medium-sized buildings powered by renewable energy sources. The implementation limitations of this type of systems, depending on the profile of the building, were identified and guidelines were presented to assess low-cost solutions dedicated to small buildings and considering the actual conditions of existing systems. Statistical analyzes were conducted for the energy demand profiles of 15 different buildings. The analyzes consisted of the preparation of box plots for each hour of working days and the calculation of the relative standard deviation (RSD) index for annual profiles of 60 min periods. The analyzes showed that the RSD index has low values for commercial buildings (e.g., hospital 7% and bank 15%) and very high values for residential buildings—even over 100%. On this basis, it can be concluded about the usefulness of energy profiles for demand forecasting. The novelty of the proposed method is to examine the possibility of using measurement data as data to forecast energy consumption based on statistical analysis, dedicated to low-cost EMS system solutions. [ABSTRACT FROM AUTHOR]

تعد دراسة استهلاک الطاقة الکهربائية في مدينة الخارجة على قدرٍ کبيرٍ من الأهمية؛ وذلک لأنَّها تعتمد على مصدرٍ واحدٍ للتغذية بالطاقة الکهربائية، يتمثل في محطة محولات نجع حمادي، بالإضافة إلى بعد مدينة الخارجة عن مسارات الشبکة الکهربائية الموحدة؛ لذلک يهدف هذا البحث إلى دراسة الوضع الحالي لاستهلاک الطاقة الکهربائية في مدينة الخارجة، من خلال الوقوف على تاريخ دخول الکهرباء إليها، بالإضافة إلى دراسة مکونات شبکة نقل الکهرباء وتوزيعها في المدينة، وکذلک دراسة استهلاک الطاقة الکهربائية، وکذلک دراسة التطور الکمي لأعداد المشترکين، ودراسة متوسط نصيب الفرد من الکهرباء المستهلکة، بالإضافة إلى دراسة مستقبل استهلاک الطاقة الکهربائية في مدينة الخارجة، من خلال الاعتماد على الطاقة الشمسية، بهدف إيجاد مصدر متجدد للطاقة الکهربائية في المدينة، وقد توصلت الدراسة إلى: - يأخذ نمط التوزيع الجغرافي لبعض مکونات شبکة توزيع الکهرباء وفقًا لمعامل صلة الجوار نمطًا متقاربًا متجهًا ناحية العشوائي، باستثناء خطوط الجهد المتوسط التي تأخذ نمطًا مشتتًا أو بصورةٍ أدق يأخذ نمطًا متباعدًا في المسافات، نظرًا لتحکم مجموعة من العوامل في هذا التوزيع هي: عدد السکان، وتنوع الأنشطة الاقتصادية، وشبکة الطرق، والمساحات الخالية. - زيادة حجم الطاقة الکهربائية المستهلکة في المدينة بمعدلاتٍ مرتفعةٍ، حيث بلغت نسبة التغير في حجم الکهرباء المستهلکة 70.9% خلال الفترة (2010- 2019)، وهي بذلک تقترب من خمسة أضعاف الزيادة السکانية البالغة 14.9% خلال الفترة نفسها. - عدم التوزان في حجم الکهرباء المستهلکة بقطاعات الاستهلاک المختلفة في المدينة، حيث استحوذ القطاعان المنزلي والحکومي على 79.9% من إجمالي الکهرباء المستهلکة في المدينة عام 2019، بينما بلغت کمية الکهرباء المستهلکة بالقطاعات الاقتصادية 10.3% من إجمالي الکهرباء المستهلکة خلال العام نفسه، ويؤکد ذلک على ضرورة توجيه استهلاک الکهرباء في المدينة بما يحقق التنمية الاقتصادية. - ارتفاع ملائمة المدينة لإنشاء المحطات الشمسية المرکزية المرتبطة بالشبکة الکهربائية الموحدة؛ نظرًا لزيادة المتوسط السنوي لعدد ساعات سطوع الشمس البالغة 12.2 ساعة/ يوم خلال الفترة (1983- 2019)؛ وانعکس ذلک على زيادة معدلات الإشعاع الشمسي المباشر البالغ 26.2 ميجا جول/م2/ يوم، بالإضافة إلى توفر مساحات کبيرة من الأراضي الصحراوية غير المأهولة. The importance of studying Electrical Energy Consumption in Al Kharja City is twofold: first, the city relies on a single source of electrical energy, which is Naga Hammadi transformer station; second, it is located so far from the paths of the Unified Electrical Network. Therefore, this research aims at studying the current situation of Electrical Energy Consumption in Al Kharja City by identifying the date of electricity entry, studying the components of the electricity transmission and distribution network, studying electrical energy consumption, studying the quantitative development of the number of subscribers, studying the average per capita electricity consumption, and studying the future of electrical energy consumption in the city relying on solar energy, thus aiming at finding a renewable source of electrical energy in the city. - According to the neighborhood link coefficient factor, the geographical distribution pattern of some components of the electricity distribution network takes a convergent pattern orienting randomly, except the medium voltage lines, that take a dispersed or divergent pattern in distances due to a number of factors, most important of which are: the population, the diversity of economic activities, the transportation network, and the vacant spaces. - The volume of electrical energy consumption in the city increased highly, as the rate of change in the volume of consumed electricity reached 70.9% from 2010 to 2019, close to five-fold the population increase that reached 14.9% in the same period. - There is imbalance in the volume of consumed electricity in the various sectors of consumption in the city, whereas the domestic and governmental sectors are 79.9% of the total electricity consumed in the city in 2019; while the amount of electricity consumed in the economic sectors amounted to 10.3% of the total electricity consumed in the same year. Thus, it is necessary to direct electricity consumption in the city to achieve economic development. - The city is a most suitable place for constructing central solar stations linked to the unified electric grid. This is due to the fact that the annual average rate of sunshine hours increased by 12.2 hours/day from 1983 to 2019. This was reflected in the increase in direct solar radiation rates of 26.2 megajoules / m 2 / day in addition to the availability of large areas of uninhabited desert lands.

Highlights: What are the main findings? The calculated HI, UTCI and THW indices show that thermal difference between the city center and the peri-urban area is greater in terms of perceived temperatures (thermal comfort indices) than in terms of air temperatures (UHI) at night in summer, especially during the heat wave period. A correlation between temperature distribution and electrical energy consumption was found. In fact, During the hot period, the overconsumption of electricity can reach 400% in the downtown area. What is the implication of the main finding? The improvement of thermal comfort is possible by promoting natural ventilation. reducing the UHI would reduce the consumption of electrical energy in summer. This study follows on from the work on the urban climate of Sfax and its energy repercussions. The spatial configuration of the urban heat island (UHI) and the spatial distribution of the thermal comfort indices, the heat index (HI), the universal thermal climate index (UTCI) and the temperature humidity wind index (THW), resulting from the car survey method, essentially show the impact of land use and the distance from the coastline on the spatial distribution of air temperatures. A maximum difference of 7 °C between the city centre and the countryside is measured in very hot summer weather. During the night phase, the apparent temperatures are oppressive, particularly in the city centre, where HI and THW comfort indices exceeding 40 °C were calculated. Compared to the HI and UTCI, the THW demonstrates a much stronger variation due to the influence of wind between open areas and the downtown area. The intensity of thermal discomfort decreases as one moves towards the rural area (−15 °C compared to the centre), hence the impact on the consumption of electrical energy dedicated to air conditioning. The central part is the most energy-intensive area compared to the peripheral areas, especially during hot days when the use of air conditioners becomes constant, day and night. [ABSTRACT FROM AUTHOR]

The rate of methomyl insecticide removal from synthetic wastewater by electrocoagulation was studied using a cell that contains a vertical serpentine tube Al anode placed between two vertical stainless steel screen cathodes. Besides the high area per unit volume of the anode, the inner surface of the serpentine tube anode has the potential of acting as a built-in cooler to remove excess heat which may adversely affect the rate of electrocoagulation, especially in high current large-scale cells which treat low conductivity waste solutions. Variables studied were initial methomyl concentration, current density, pH, NaCl concentration, temperature, and degree of stirring. Under optimum conditions, 100% of the methomyl was removed in 40 min. The % COD removal was found to increase with increasing all the variables investigated, except for stirring where the % COD removal increases up to a certain limit beyond which increasing the degree of stirring did not affect the % COD removal. Electrical energy consumption ranged from 0.11 to 0.65 kW∙h/kg of the removed methomyl depending on the operating conditions. The importance of the results in treating wastewater discharged by insecticide plants and agricultural runoff water was highlighted. [ABSTRACT FROM AUTHOR]

As the use of fossil energy sources in transport declines, new technologies, e.g., electric vehicles, are being introduced. One of the advantages of electric vehicles in urban driving is the possibility to charge their batteries with regenerative energy during braking. For this reason, electric cars usually have a longer range per charge in urban driving than in non-urban driving. This research experimentally examined the regenerative braking of a converted Renault Clio electric car at different regenerative braking settings in the range of 0–100%. An original research methodology was developed for road tests in urban driving. The driving cycle included aggressive driving with rapid acceleration and braking. The road test was conducted in second and third gears, which are the usual gears for driving an electric car in a city. The highest regenerative braking efficiencies were achieved at a 100% setting, which in some replications reached 24% of the total electrical energy consumed; however, the 100% setting was too high from the perspective of comfortable driving of the electric car and contributed to a too significant increase in the braking force at the initial stages of braking. [ABSTRACT FROM AUTHOR]

This paper presents energy and reliability analyses of eight wastewater treatment plants (WWTPs) operating in small communities in Ontario, Canada, with rated capacities ranging from 60 to 4,400 m3/d. Five different treatment technologies were investigated, namely, rotating biological contactor (RBC), sequencing batch reactor (SBR), membrane bioreactor (MBR), lagoon, and extended aeration activated sludge process (EAAS). Energy benchmarking based on key performance indicators (KPIs) was used to quantify the specific consumption of energy in WWTPs per unit of the pollutant removed. The overall annual electrical energy consumption was correlated to the volume of treated wastewater, the population equivalent, and the amounts of TSS and BOD removed. The RBC plants showed a distinctive advantage for all energy KPIs assessed, while SBR plants yielded the highest values of energy KPIs. Analyses of the expected percentage of compliance with discharge standards and the coefficient of reliability (COR) based on the WWTPs' performance records showed that few WWTPs were able to achieve reliability levels over 95%, considering the mandated discharge standards under the current operating and maintenance conditions. Within each technology, the treatment train, operating conditions, maintenance level, and age of infrastructure were important elements that contributed to the large variability observed. HIGHLIGHTS Small WWTPs show limited data and often perceived of low environmental impact.; KPIs provide a helpful tool to assess WWTPs' energy use.; Lagoons followed by RBCs had the lowest consumption as per energy KPIs.; Operation and maintenance impact reliability levels for BOD, TAN, and TSS removals.; Reliability concept is useful in evaluating WWTP performance and setting standards.;

This paper analyzes the relationship between electricity consumption and economic growth, as well as the determinants of this relationship, in the countries of the West African Economic and Monetary Union (WAEMU) over the period from 1990 to 2018. The results obtained from the Autoregressive Lagged Model (ARDL) show that, in the long run, economic growth, the degree of international openness, and gross fixed capital formation explain electricity consumption. In the short run, no variable affects this consumption. Furthermore, in the country-by-country model, the results indicate differentiated effects. They show, in the long and short term, the absence of the effects of economic growth on electrical energy consumption. [ABSTRACT FROM AUTHOR]

Large space buildings, such as airport terminal buildings, consume high energy due to the cooling demand; especially in hot arid climates; to achieve passenger thermal comfort. Evaluation of thermal environment and energy analysis inside airports is an important issue, in terms of reducing energy and controlling indoor environments. This work aims to analyze electrical energy consumption and the indoor environment of the terminal building at Assiut International Airport, Egypt as a case study. Indoor environmental conditions, and energy consumption with HVAC systems, were monitored inside five multiple functional zones within the terminal building (Departure hall, Public area, Duty-free hall, Gate, and Arrival hall) during summer (August 2017). The indoor environmental parameters monitored include: temperature, relative humidity, and illuminance. The results indicate the HVAC system consumed the largest portion (70%) of the overall building electricity demand, and the Energy Use Intensity (EUI) of ATZ terminal building is 300 kWh/m2/year. Also, indoor temperatures were found to be within the acceptable range of comfort, with a high energy consumption of 6,000,000 kWh for the year 2017. A significant reduction of monthly energy consumption was achieved based on increasing HVAC set temperature from 25 °C to 27 °C; a reduction equal to 24.5% of total energy consumption during the hot months. The results of this study help search for sustainable solutions for reducing energy consumption and improving passenger thermal comfort inside the terminal building (phase 2).

A convolutional neural network does not require to be stable when it is used for economy applications being related with the offline learning. Nevertheless, a convolutional neural network requires to be stable when it is used for economy applications being related with the online learning. Therefore, it would be interesting to ensure the stability of a convolutional neural network for economy applications being related with the online learning. In this investigation, a stable algorithm considering a time varying learning rate is proposed to adapt the weights of a stable convolutional neural network. The stable algorithm considering a time varying learning rate is used to improve the learning and to ensure the stability and robustness of the stable convolutional neural network, where the time varying learning rate will obtain big steps when the minimum of the cost function is far, and the time varying learning rate will obtain small steps when the minimum of the cost function is near. The stable convolutional neural network is compared with the principal component analysis neural network, non-negative matrix factorization neural network, and convolutional neural network for economy applications being related with the online learning considering the electrical energy consumption modeling and hybrid chiller modeling. • A stable algorithm is proposed for a stable convolutional neural network. • The stable algorithm is used to ensure the stability and robustness. • The stable convolutional neural network is applied for economy applications. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, graphitic carbon nitride was used as a support for CuO/TiO2 nanocomposite and was used for the photodegradation of ketoprofen (KP) in various water qualities. The formation of the CuO/TiO2@GCN nanocomposites was proven using different characterization methods. PXRD and Raman analyses of TiO2 and CuO/TiO2 showed that the composite peaks shifted relative to those of TiO2, showing interactions between the two materials. The morphology of CuO/TiO2@GCN consisted of CuO/TiO2 nanoparticles on the surface of the mat‐like GCN. CuO/TiO2@GCN had a narrower band gap and its photoluminescence emission peak was lower than of TiO2. CuO/TiO2@GCN was found to be the best performing photocatalyst that could be used over 3 cycles without major loss in activity. Also, the material was shown to be able to photocatalyze KP in groundwater. The by‐products of the photodegradation were identified by LC–MS. The electrical energy consumed for this process was 2.66E5 kWh/m3. The superior photocatalytic efficiency of CuO/TiO2@GCN relative to TiO2 was attributed to the increased charged separation and that light of higher wavelength was harvested. [ABSTRACT FROM AUTHOR]

Photocatalytic membranes possessing both photocatalytic and solid-liquid separation capabilities were developed. These materials are based on ternary 1% CuO/TiO2@GCN (1:9) embedded on cellulose acetate (CA) via the phase inversion method. The CA membranes containing 0.1, 0.3 and 0.5 wt% of 1% CuO/TiO2@GCN (1:9) (CTG–100, CTG–300 and CTG–500) were fabricated. The deposition of 1% CuO/TiO2@GCN (1:9) onto the CA membranes and the consequential changes in the materials' properties were investigated with various characterization techniques. For instance, PXRD, FTIR, and XPS analysis provided evidence that photocatalytic membranes were formed. Electron microscopy and EDX were then used to visualize the photocatalytic membranes and show that the photocatalyst (1% CuO/TiO2@GCN (1:9)) was well dispersed onto the CA membrane. On the other hand, the properties of the photocatalytic membranes were scrutinized, where it was found that the membranes had a sponge-like morphology and that was significantly less hydrophilic compared to neat CA. The removal of KP in water using CTG–500 exhibited over 94% efficiency, while 38% for neat CA was achieved. Water permeability flux improved with increasing 1% CuO/TiO2@GCN (1:9) and hydrophilicity of the membranes. The electrical energy consumption was calculated and determined to be significantly lower than that of the CA membrane. The CTG–500 membrane after every cycle showed self-cleaning ability after operation in drinking and groundwater. [ABSTRACT FROM AUTHOR]

The implementation of energy efficiency measures and use of renewable energy sources in educational buildings can significantly contribute to reducing energy consumption, but also to CO2 emissions in the entire public sector. The paper shows the comparison of energy consumption indicators for 61 elementary school buildings which have previously been divided in 12 groups, according to the period of construction and size, based on the national typology called TABULA, as the first step of further study on how to use the renewable energy sources. The aim of this paper is to use the energy benchmarking process to select representative facilities which are suitable for applying renewable energy sources, for their further energy efficiency improvement. Indicators of annual specific electricity consumption and CO2 emissions per unit area [kWh/m2] and per user [kWh/user] were calculated. After that, from two groups (in which the highest electricity consumption and CO2 emissions are 68.37% and 74.53% of the total consumption/ emissions), one representative facility was selected.

Energy consumption is the biggest obstacle in the economic growth of a country. In recent years, Turkey has imported around at the rate of three-quarters of its total energy demand. Upon the past 10-years running, Turkey paid nearly half a trillion dollars for its total energy bill. The big share of energy consumption has emerged from buildings. Therefore, energy savings have great importance, particularly in the buildings. A refrigerator is responsible for the most dominant electrical energy consumption rate with 32% in a house. Therefore, this paper proposes a novel household refrigerator design for reducing energy consumption. In the proposed design, the necessary air for the cooling process will be provided from outdoor ambient in appropriate weather condition. The compressor work will, thus, be decreased via this way, and contribute to a reduction in energy consumption. The results indicated that this system in 63 provinces can be effectively used between 1 and 4 months and help to reduce 36 million $ in Turkish electric energy bill with the use of only 1 year period. Additionally, a reduction of approximately 850,000 tons of CO2 annually in Turkey can be achieved by applying the proposed design in this study. Hereby, Turkey can contribute not only to be sustained economic growth but also to reduce harmful gas emissions arising from electricity generation methods in the country.

An artificial neural network (ANN) approach with response surface methodology (RSM) technique has been applied to model and optimize the removal process of Brilliant Green dye by batch electrocoagulation process. A multilayer perceptron (MLP) - ANN model has been trained by four input neurons which represent the reaction time, current density, pH, NaCl concentration, and two output neurons representing the dye removal efficiency (%) and electrical energy consumption (kWh/kg). The optimized hidden layer neurons were obtained based on a minimum mean squared error. The batch electrocoagulation process was optimized using central composite design with RSM once the ANN network was trained and primed to anticipate the output. At optimized condition (electrolysis time 10 min, current density 80 A/m2, initial pH 5 and electrolyte NaCl concentration 0.5 g/L), RSM projected decolorization of 98.83% and electrical energy consumption of 14.99 kWh/kg. This study shows that the removal of brilliant green dye can be successfully carried out by a batch electrocoagulation process. Therefore, the process is successfully trained by ANN and optimized by RSM for similar applications.

This study follows on from the work on the urban climate of Sfax and its energy repercussions. The spatial configuration of the urban heat island (UHI) and the spatial distribution of the thermal comfort indices, the heat index (HI), the universal thermal climate index (UTCI) and the temperature humidity wind index (THW), resulting from the car survey method, essentially show the impact of land use and the distance from the coastline on the spatial distribution of air temperatures. A maximum difference of 7 °C between the city centre and the countryside is measured in very hot summer weather. During the night phase, the apparent temperatures are oppressive, particularly in the city centre, where HI and THW comfort indices exceeding 40 °C were calculated. Compared to the HI and UTCI, the THW demonstrates a much stronger variation due to the influence of wind between open areas and the downtown area. The intensity of thermal discomfort decreases as one moves towards the rural area (−15 °C compared to the centre), hence the impact on the consumption of electrical energy dedicated to air conditioning. The central part is the most energy-intensive area compared to the peripheral areas, especially during hot days when the use of air conditioners becomes constant, day and night.

Large space buildings, such as airport terminal buildings, consume high energy due to the cooling demand; especially in hot arid climates; to achieve passenger thermal comfort. Evaluation of thermal environment and energy analysis inside airports is an important issue, in terms of reducing energy and controlling indoor environments. This work aims to analyze electrical energy consumption and the indoor environment of the terminal building at Assiut International Airport, Egypt as a case study. Indoor environmental conditions, and energy consumption with HVAC systems, were monitored inside five multiple functional zones within the terminal building (Departure hall, Public area, Duty-free hall, Gate, and Arrival hall) during summer (August 2017). The indoor environmental parameters monitored include: temperature, relative humidity, and illuminance. The results indicate the HVAC system consumed the largest portion (70%) of the overall building electricity demand, and the Energy Use Intensity (EUI) of ATZ terminal building is 300 kWh/m2/year. Also, indoor temperatures were found to be within the acceptable range of comfort, with a high energy consumption of 6,000,000 kWh for the year 2017. A significant reduction of monthly energy consumption was achieved based on increasing HVAC set temperature from 25 °C to 27 °C; a reduction equal to 24.5% of total energy consumption during the hot months. The results of this study help search for sustainable solutions for reducing energy consumption and improving passenger thermal comfort inside the terminal building (phase 2). [ABSTRACT FROM AUTHOR]

Central air conditioning is the main energy-consuming equipment in modern large-scale commercial buildings. Its energy consumption generally accounts for more than 60% of the electricity load of an entire building, and there is a rising trend. Focusing on reducing central air conditioning energy consumption is a first priority to achieve energy savings in modern large-scale commercial buildings. To study the main influencing factors of central air conditioning energy consumption in large shopping malls, in-depth collection and analysis of energy consumption data of Shenzhen Tian-hong shopping mall were considered, and the impact of factors such as the basic composition of central air conditioning, time, and Shenzhen weather on the energy consumption of shopping malls was considered. The most representative Buji Rainbow store of the Rainbow Group is used as the research object. The influencing factors of central air conditioning on its energy consumption are divided into air conditioning pumps, host 1–1, host 1–2, host 2–1, and host 2–2. The power consumption of the freezer and the eight impact indicators of time and weather in Shenzhen were constructed using Pearson correlation coefficients and a long short-term memory neural network method to construct a regression model of the energy consumption prediction of the mall building. The average relative deviation between the predicted energy consumption values and the measured energy consumption values is less than 10%, which indicates that the main influencing factors selected in this paper can better explain the energy consumption of the mall, and the obtained energy consumption prediction model has high accuracy. [ABSTRACT FROM AUTHOR]

As the use of fossil energy sources in transport declines, new technologies, e.g., electric vehicles, are being introduced. One of the advantages of electric vehicles in urban driving is the possibility to charge their batteries with regenerative energy during braking. For this reason, electric cars usually have a longer range per charge in urban driving than in non-urban driving. This research experimentally examined the regenerative braking of a converted Renault Clio electric car at different regenerative braking settings in the range of 0–100%. An original research methodology was developed for road tests in urban driving. The driving cycle included aggressive driving with rapid acceleration and braking. The road test was conducted in second and third gears, which are the usual gears for driving an electric car in a city. The highest regenerative braking efficiencies were achieved at a 100% setting, which in some replications reached 24% of the total electrical energy consumed; however, the 100% setting was too high from the perspective of comfortable driving of the electric car and contributed to a too significant increase in the braking force at the initial stages of braking.

Energy demand management is particularly important for developing and emerging economies. Their energy consumptions increase significantly, depending on their growing economies. As a result of Turkey's rapid economic and population growth, electricity consumption is increasing. Electricity consumption forecasting plays an essential role for energy suppliers, consumers, and policy makers. Therefore, using models to accurately and reliably forecast future electricity consumption trends is a key issue for the planning and operation of electric power systems. This paper focused on forecasting electrical energy consumption by utilizing deep learning methods, i.e., Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, for time series data. One-hour and three-hour ahead forecasting are accomplished by using a historical dataset of electrical energy consumption in Turkey. The comparison results show that the GRU model is slightly better than that of the LSTM. Our study also reveals that one-hour ahead predictions are more accurate than three-hour ahead predictions. [ABSTRACT FROM AUTHOR]

Increasing electrical energy consumption in a region due to economic development must be anticipated as early as possible in order to ensure that electricity is available sufficiently in affordable price. One method for the anticipation is forecasting electrical energy consumption. This paper aims to forecast electrical energy consumption using Long-range Energy Alternatives Planning system (LEAP) software for study case in Bireuen District. Based on historical data since 2012 in form of population development, number of customers, electrification ratio, energy intensity, and profile of electrical energy consumption, an analysis is carried out to forecast the future electrical energy demand in Bireuen District until 2026. The analysis predict that in 2026, the number of electricity customers will reach 183,016 customers with an average growth of 4.7% per year, and electricity consumption will reach 563.02 GWh with an average growth of 10.67% per year. In addition, the electrification ratio will continue to increase to 99.42% in 2026 while the energy elasticity will continue to decline to reach 0.61 in that year.

Photocatalytic membranes possessing both photocatalytic and solid-liquid separation capabilities were developed. These materials are based on ternary 1% CuO/TiO2@GCN (1:9) embedded on cellulose acetate (CA) via the phase inversion method. The CA membranes containing 0.1, 0.3 and 0.5 wt% of 1% CuO/TiO2@GCN (1:9) (CTG–100, CTG–300 and CTG–500) were fabricated. The deposition of 1% CuO/TiO2@GCN (1:9) onto the CA membranes and the consequential changes in the materials’ properties were investigated with various characterization techniques. For instance, PXRD, FTIR, and XPS analysis provided evidence that photocatalytic membranes were formed. Electron microscopy and EDX were then used to visualize the photocatalytic membranes and show that the photocatalyst (1% CuO/TiO2@GCN (1:9)) was well dispersed onto the CA membrane. On the other hand, the properties of the photocatalytic membranes were scrutinized, where it was found that the membranes had a sponge-like morphology and that was significantly less hydrophilic compared to neat CA. The removal of KP in water using CTG–500 exhibited over 94% efficiency, while 38% for neat CA was achieved. Water permeability flux improved with increasing 1% CuO/TiO2@GCN (1:9) and hydrophilicity of the membranes. The electrical energy consumption was calculated and determined to be significantly lower than that of the CA membrane. The CTG–500 membrane after every cycle showed self-cleaning ability after operation in drinking and groundwater.

One of the modern and effective tools for energy efficiency improvement at the level of national economies is management of the demand for electrical energy consumption. The mechanism of management of the demand for electrical energy consumption has a significant potential for energy efficiency improvement for the Russian economy, but due to structural features of the Unified Energy System of Russia, the Electrical Energy Demand Management Program is at the stage of concept development. A model of management of the demand for electrical energy consumption for Unified Energy System of Russia has been developed taking into account structural features of the electric power system. Peculiarities of the economic structure of Russia, which influence the formation of the structure of the country’s electric power complex, have been revealed. They were taken into account when developing requirements for the electrical energy demand management system in the Unified Energy System of Russia. The basic features are the multilevel form and hierarchy of the structure; they have been investigated in the process of developing the demand management model. The classification of electric power industry entities, related to processes of electric energy circulation and the influence on the management of the demand for electrical energy consumption, has been developed with economic interests of each entity within the framework of the demand management model. The electrical energy demand management model, which is based on the hierarchical structure of demand management, has been developed and covers the whole complex of management functions and takes into account features of demand management at each management level. The model allows to significantly improve the efficiency of management of the demand for electrical energy consumption, to ensure the quality of management.

• Combination of US, O 3 and EC process for removal of color, COD with electrical energy consumption from landfill leachate was developed. • US/O 3 /EC was highly successful in removal of color and COD than other hybrid and single processes such as O 3 /EC, US/EC, US/O 3 and EC, O 3 , US. • Maximum color and COD removal efficiencies were obtained using US/O 3 /EC with required electrical energy consumption. • Operating parameters have been studied to obtain the hybrid process performance efficiency. The combination of advanced oxidation processes (AOPs) and electrochemical process is gaining growing popularity for use in wastewater treatment. The present work explores for the first time, treatment of leachate wastewater from landfill waste using a combination of sonication (US), ozonation (O 3) and electrocoagulation (EC) process expressed by % color removal, % Chemical Oxygen Demand (COD) removal and its associated electrical energy consumption. The results revealed that, the combined process (US/O 3 /EC) is highly successful in the treatment of leachate wastewater from landfill in terms of % color removal (100 %) and % COD removal (97.50 %) with low electrical energy consumption (8kWhr/m3) than the other hybrid and single process such as O 3 /EC, US/EC, US/O 3 and EC, O 3 , US. The influence of different process parameters such as electrolyte concentration (1.50–7.50 g/L), current density (1.25–7.50 A/dm2), initial effluent pH (2–11), COD concentration (2000–6000 ppm), O 3 production (0.75–3.75 g/hr) and sonication power (20–100 Watts) were studied on the efficiency of pollutant removal evaluated by reduction of % color and % COD and electrical energy consumption using hybrid US/O 3 /EC process. Overall, the analysis clearly showed that the approaches to integrated treatment technologies could synergistically remove the pollutant from the wastewater. [ABSTRACT FROM AUTHOR]

In this study, the extent and effect of fluctuations in the proximate composition of carbon materials on specific electrical energy consumption and exergy efficiency in the production process of silicon furnaces was evaluated using a novel artificial neural network approach. The neural network architecture 4-5-2 with hyperbolic sigmoid in the hidden layer and linear function in the output layer was used. The proposed model successfully predicts the values of the specific electrical energy consumption and the exergy efficiency through the correlation coefficient (0.93 and 0.87) of the actual and predicted values for the various proximate compositions of carbon materials. The interactive effects of components in the mixtures of carbon materials were investigated via contour diagrams. Fixed carbon had the biggest impact on electrical energy consumption and exergy efficiency in the mixtures of carbon materials, followed by moisture. Compared with volatile matter from woodchips without any pretreatment, volatile matter from petcoke, charcoal, and coal had a significant effect on electrical energy consumption and exergy efficiency. [ABSTRACT FROM AUTHOR]

With the increase of electrical energy consumption and associated power grid expansion the complexity in the grid network increases which tends to increase the fault occurrence possibilities. Low voltage ride‐through capability helps the system to achieve the standard of grid code by remaining connected with the grid during fault occurrence. Direct current‐based saturated core fault current limiter (DC‐SCFCL) is a type of fault current limiter with superior influence in fault ride‐through (FRT) capability. It has an arrangement of embedded alternating current coil and direct current (DC) coil across the side and centre of the core limb. However, being more effective in FRT, the ideal DC‐SCFCL has some of its drawback such as development of high voltage across the DC coil and more current difference during fault occurrence. To limit these drawbacks a modified DC‐SCFCL with shorted ring and also working with the coil below its critical temperature has been proposed in this study. The voltage profile, as well as the current profile, has been analysed which comprises its behaviour in all conditions. The proposed technique is applied to DFIG‐based wind energy system to restrict the fault current and enhance the FRT capability. [ABSTRACT FROM AUTHOR]

Consumption patterns of residential building in Kingdom of Saudi Arabia (KSA) with specific reference to Qassim region are analysed to establish energy consumption indicators including consumption per dwelling, per capita and per unit area. The average shares of lighting, domestic appliances, water heaters and air conditioning out of total yearly consumption are determined to be 8.5%, 14.7%, 10.3% and 66.5%, respectively. Parametric analysis shows that residential energy consumption is insignificantly influenced by the type of building except that villas have somewhat higher consumption during the hot summer season. However, residential energy consumption is considerably affected by occupant behaviour, meteorological conditions, as well as the technical conditions of both building envelopes and air conditions setting temperature point. Energy efficiency measures are also discussed, and opportunities for energy saving are proposed. Potential energy savings up to 43% in air conditioning cooling load could be achieved by applying the KSA standard of envelope insulation. Total residential consumption savings of 3% could also be achieved by using efficient lighting and savings of 11.1% by adjusting the temperature setting point of air conditioners to 24 °C instead of 22 °C. [ABSTRACT FROM AUTHOR]

Decolorization of Reactive Red 239 (RR239) using a sulfate radical (SO4.-)-based advanced oxidation process was investigated in a batch photoreactor. SO4.- was generated in situ through activation of peroxydisulfate under UV-C illumination. Effect of initial pH (3-9), initial dye concentration (20-50 mg dm-3), S2O82- dosage (0-3 mmol dm-3) and lamp power (0-16 W) were explored. It was found that initial pH of RR239 solution had no considerable effect on decolorization efficiency. Experimental results demonstrated that decolorization efficiency enhanced with increasing S2O82- dosage and lamp power and decreasing initial dye concentration. Quenching experiments were performed with alcohols to determine the dominant radical. Under the conditions tested in this study, 98% and 70% decolorization and aromatic degradation efficiencies were obtained within 120 min of irradiation time, respectively. Decolorization and aromatic degradation efficiencies of RR239 in PS/UV-C and PMS/UV-C systems were evaluated in terms of electrical energy consumption per order by figure of merit approach. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid strategy based on intelligent approaches to forecast electricity consumptions. The proposed forecasting strategy includes three main steps: (a) the evaluation of a correlation coefficient for socio-economic indicators on electric energy consumptions, (b) the classification of historical and socio-economic indicators using the proposed feature selection method, (c) the development of a new combined method using Adaptive Neuro-Fuzzy Inference System and Whale Optimization Algorithm to predict electrical energy consumptions. The simulation results have been tested and validated by real data sets achieved within 1992 and 2010 in two pilot cases in a developing country (Iran) and a developed one (Italy). The research findings pinpointed the greater accuracy and stability of the new developed method for electrical energy consumption forecasting compared to existing single and hybrid benchmark models. [ABSTRACT FROM AUTHOR]