Decrease In Electricity Consumption Research Articles

Selection of tropical plants for an extensive green roof with abilities of thermal performance, energy conservation, and greenhouse gas mitigation

The development of green roofs to mitigate urban heat island effect is critical for conserving energy and reducing carbon footprint. This study evaluated the thermal performance of an extensive green roof design compared with a conventional roof and assessed the suitability of thirty-two plants for low-maintenance green roofs in a tropical area. Experiments were carried out in four phases, covering the tropical climatic conditions from June 2016 to December 2017. The results indicate that a green roof temperature was significantly lower than that of a conventional roof by 12ºC. This led to an 84% reduction in heat transfer through the building, potentially leading to a decrease in electricity consumption by 0.20 kWh/m2/8h and saving electricity costs by 0.019 USD/m2/8h, achieved through a reduction in the cooling load on air conditioners. The highest carbon dioxide removal achieved in this study was 3.01 kgCO2/m2, with an effective mitigation of greenhouse gases by 28.46 kgCO2eq/m2/year. The top three plant species recommended for a low-maintenance green roof are Dracaena cochinchinensis, Santisukia kerrii, and Dracaena kaweesakii. The plant characteristics for low-maintenance green roofs in tropical climates include drought and extreme weather tolerance, disease and insect resistance, short and spreading roots, succulent leaves with ability to store water, low water requirements, slow growth rate, easy availability locally and affordability, thriving in low-nutrient conditions, and a high evapotranspiration rate. In applicable contexts, green roofs designed with suitable tropical plants could potentially enhance urban environments and contribute to achieving low-carbon and environmental sustainability.

A comprehensive study on electricity utilization and CO2 emissions in Sami Laris Swalayan, Cilacap, Indonesia

In today's world, with an increased focus on environmental awareness and sustainability, mitigating carbon footprint has emerged as a crucial goal for businesses across various sectors, including supermarket chains. This research centers on Sami Laris Swalayan, a prominent and widely recognized supermarket chain, to comprehensively analyze its electricity utilization patterns and the corresponding carbon dioxide (CO2) emissions. By delving into electricity utilization data spanning a specific period, this study identifies peak demand periods, explores energy-efficient opportunities, and assesses the potential for integrating renewable energy sources. The research underscores the significance of reducing CO2 emissions, advocating sustainable energy practices within the supermarket chain, contributing to environmental conservation efforts, and fostering a culture of responsible energy management. The investigation begins with a thorough analysis of Sami Laris Swalayan's electricity utilization, seeking to understand the varying trends and patterns in energy consumption. The data above shows the fluctuating electricity consumption of the company, with the highest figure recorded in April, reaching 26,965 kWh. The high electricity usage is attributed to the total operational hours from 09:00 to 21:00 and the addition of other facilities such as the accessory room, bakery outlet, and coffee outlet. In contrast, the lowest consumption was observed in February, with 23,796 kWh, which was influenced by the implementation of Large-Scale Social Restrictions (PSBB) at the end of January 2021. During this period, the operational hours were shortened from 09:00 to 19:00, leading to a decrease in electricity consumption the following month.

Impact of the feed-in-tariff exemption on energy consumption in Japanese industrial plants

To promote renewable energy deployment, Japan introduced a feed-in tariff policy in 2012, financed through a surcharge on electricity prices for consumers. The Japanese government also offered a discount system for electricity-intensive industrial plants, exempting them from paying full surcharges. Using monthly plant-level data from 2005 to 2018, this study evaluated the exemption system’s impact on electricity and fossil fuel consumption for plants in the iron and steel, chemical products, and pulp and paper sectors. Our results show that the exempted iron and steel plants decreased electricity purchase and consumption 18.62% and 17.88% after 2017, respectively. This decrease is attributed to the introduction of an electricity efficiency after 2017. It is worth noting, however, that the revision of the scheme did not affect the chemical, pulp and paper sectors. In this sense, a stronger efficiency requirement might be warranted to ensure the decrease in electricity consumption for all sectors. Nevertheless, we estimate that roughly 7 million tCO2 have been avoided thanks to the decrease in electricity consumption in the iron and steel sector.

Performance analysis of fluorescent and LED lighting sources

"Artificial lighting is a necessity in the conduct of activities, especially at night. The use of fluorescent and LED lighting sources has led to a decrease in electricity consumption compared to incandescent lighting sources. The paper presents a case study highlighting the voltage and current waveforms in the operation of the analyzed sources. A considerable distortion of the current waveform is found, identifying the need for measures to improve power quality. The photometric measurements showed that for some light sources the luminous flux on the box is higher than the luminous flux actually emitted. Keywords: lighting sources, electrical and photometric measurements, waveforms, efficiency"

Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0

Accelerating the decarbonisation of the built environment necessitates increasing electrification of end-uses, which in turn poses the issue of rethinking the role of energy efficiency in conjunction with flexibility in grid interaction. This requires a better understanding of the electricity load profiles at hourly or sub-hourly intervals using techniques that are simple, reliable, and interpretable. To this extent, this study proposes a reformulation of the Time Of Week and Temperature modelling approach. This approach is able to separate the energy consumption dependence on building operational characteristics (Time Of Week) and on weather (outdoor air temperature), through a highly automated modelling workflow, necessitating minimal effort for model tuning. These features, along with its intrinsic interpretability due to its formulation using multivariate regression and the availability of open-source software, makes it an ideal starting point for applied research. The case study selected for the research is a fully electrified public building in Southern Italy. The building has been monitored for 5 years, before, during and after the COVID-19 lockdown. The novel model formulation is calibrated using hourly interval data with a Coefficient of Variation of Root Mean Square Error in the range of 20.0–28.5% throughout the various monitoring periods. The counterfactual analysis of electricity consumption indicates a 10.7–26.7% decrease in electricity consumption due to operational adjustments following COVID-19 lockdown, highlighting the impact of behavioural change. Finally, the possibility of additional workflow automation and enhanced interpretability is discussed.

Managing Electricity Consumption on Campus: The Effect of Online Learning from Home

This paper analyzes the effect of Online Learning from Home (OLFH) during the COVID-19 pandemic on electricity consumption and the quality of the learning process quality. This study identifies the reduction of electricity consumption on campus during OLFH, which can be utilized to maintain the quality of the learning process. The electricity consumption data was analyzed from the direct measurement using the electricity consumption real-time monitoring system. Again, the data analyzed of the online learning process quality was obtained from the student survey via Google Forms. The results of monitoring electricity consumption before and during OLFH showed that the most significant decrease in electricity consumption occurred in classroom buildings, with a decrease value of 68.74%. Meanwhile, the lowest reduction of electricity consumption occurred in the rectorate building, with its reduced value of 19.02%. A student survey showed that most respondents were satisfied with the transition from face-to-face learning to OLFH during the COVID-19 pandemic. However, this study also identified negative aspects of OLFH. Reducing electricity consumption during OLFH will cost saving for the university. The cost saving can be used to implement programs to address the deficiencies of OLFH and improve its infrastructure so that the quality of the learning process is maintained.

Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia

Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior.

Invisible walls: Exploration of microclimate effects on building energy consumption in New York City

Reducing greenhouse gases from buildings forms the cornerstone of policy to mitigate the effects of climate change. However, the automation of urban-scale building energy modeling systems required to meet global urban demand has proven challenging due to the bespoke characteristics of each city. One such point of uniqueness between cities is that of urban microclimate, which may play a significant role in altering the performance of energy efficiency in buildings. This research proposes a way to rapidly collect urban microclimate data through satellite readings and climate reanalysis, enabling researchers to study the invisible walls of microclimate, which play a critical role in the buildings’ energy consumption.We demonstrate the utility of this data by composing an analysis against three years of monthly building energy consumption data from New York City. Our study highlights the significance of urban microclimates in decreasing the gas consumption of some buildings in New York by 71% and increasing the gas consumption in others by as much as 221%. Microclimates also seem to be responsible for the decrease of electricity consumption by up to 28.6% in regions or increases of up to 77% consumption in others.

Life cycle assessment of reverse osmosis for high-salinity seawater desalination process: Potable and industrial water production

Seawater desalination is applied worldwide as a solution to water supply, especially for countries with limited water resources. The industry sector is the primary water user in varying qualities for different purposes. Desalination of seawater is increasingly implemented in the Persian Gulf region, but the environmental impacts have not been adequately assessed. This study assessed the life cycle environmental impacts of potable and industrial grade water with the most detailed inventory, including the impact of the brine rejected to the sea and solid wastes using SimaPro software. The 18 ReCiPe midpoint H impact categories, three endpoint damage categories, and the single score have also been specified. The seawater reverse osmosis powered by fossil fuels was studied to produce potable and industrial water. Electricity has the highest contribution to most of the studied impact categories. The sensitivity analysis showed that a 10% decrease in electricity consumption could reduce fossil resource scarcity and global warming by about 5% for potable and industrial water. The single total score for Sea water reverse osmosis (SWRO) potable and industrial water production are 98.83 and 168.54 mPt, respectively, mainly related to the human health damage category. The cumulative energy demand assessment showed that non-renewable biomass and renewable (wind, solar and geothermal) have the least energy intensity, respectively. To the best of our knowledge, the life cycle assessment of SWRO industrial water production has not been performed before. The current study would be a baseline for further comparisons. The potable water production results agree with the other studies despite having a much more detailed inventory.

Solar–Wind System for the Remote Objects of Railway Transport Infrastructure

The article deals with adding the power of a local object to a solar–wind system when consuming electricity from the grid within the power limit. The parameter’s calculation technique for the different values of degree of power increase was considered. The load schedule, the electricity consumption, use of installed power, boundary conditions of generation, and ensuring energy balance were taken into account. Furthermore, data from the renewable source’s generation archive for the location of the object were used. The control of the power consumed by the object was carried out on the taken values of added and total load power with binding to photovoltaic generation. The added power reference on some time intervals was carried out according to the actual renewable generation power value. This increases the degree of use of the battery capacity and energy from renewable sources. The reference of the added power with the state of battery charge formation was carried out according to the forecast. One cycle of deep battery discharge at the evening peak was used to extend the battery life. For the accepted conditions with the average monthly values of renewable energy sources generation with an increase in power by 1.6 times, there was a decrease in electricity consumption by 1.57–4 times.

CSES Satellite Observations of 50 Hz Power Line Radiation Over Mainland China and Its Response to COVID‐19

AbstractWe present a low‐altitude satellite survey of power line harmonic radiation (PLHR) at 50 Hz over Mainland China. We analyzed the month‐to‐month variation pattern in PLHR occurrence rate and further analyzed its correlation with some influencing factors (i.e., solar radiation, lightning flashes, and electricity consumption) using CSES satellite electric field data from 2019 to 2021. We also investigate the response of PLHR occurrence rate to COVID‐19. The statistical results show the dayside PLHR occurrence rate decreasing from winter to summer solstice and increasing from summer to winter solstice, which indicates it is controlled by the solar radiation. The nightside variation is more complex, which may be due to many sources that could influence the nightside lower ionosphere. The PLHR occurrence rate significantly decreased over Mainland China in February 2020, which is because of the significant decrease in electricity consumption due to the suspension of industrial production caused by COVID‐19.

Green Value Stream Mapping: A Tool For Increasing Green Productivity (The Case of PT. NIC)

The role of the environment in business success is increasing from day to day. One of the ways to harmonize the goals of making a profit with the environment is by achieving green productivity. Green productivity means that companies can increase their productivity and at the same time improve their environmental performance. To reach that goal one of the ways is that businesses apply value stream mapping and make all the activities to be green which is known as a Green Value Stream Mapping. A combination of secondary data collection and Focus Group Discussions involving all relevant production staffs was conducted to get the initial data and the ideas to improve the performance of electricity, LPG, and water consumption at PT NIC Semarang. Compared to the initial data of the Current State Green Value Stream Mapping, the improvements resulted from the Future State Green Value Stream Mapping were a 51.4% decrease in electricity consumption, a 24.42% decrease in LPG consumption, and a 60% decrease in water consumption. From this empirical study, two important outcomes of future implications were found. For the food industry, implementing GVSM should be adjusted in such a way that the experiments to reach the Future State Green Value Stream Mapping will not affect the quality of the final products and that FGDs are very effective to generate ideas of improvements and getting commitments from production staffs to implement the improvements

Analisis sustainability peformance pada perusahaan di bursa efek indonesia sebelum dan selama Covid 19

This study aims to explore differences in performance between the period before Covid 19 and the period during Covid 19. This study uses financial reports and corporate sustainability reports in various industries listed on the IDX as research objects and uses the Wilcoxon test as a method that compares the period before Covid 19, namely 2018 - 2019 and the period during Covid 19 in 2020. The results showed that there was a significant decrease in economic performance of 16.37, a significant increase in performance. social and environmental are 10.39 and 72.47, respectively. This decline in economic performance occurred as a result of the implementation of social distancing, restrictions on travel and tourism, and the decline in people's purchasing power. On the other hand, these regulations lead to a decrease in electricity consumption in the Java Bali area and a decrease in air pollution, especially in Jakarta. Meanwhile, the increase in social performance occurred due to the widespread assistance from companies for the surrounding community, especially those dealing with Covid 19.

ОЦЕНКА ВОЗДЕЙСТВУЮЩИХ ФАКТОРОВ И ПРОГНОЗИРОВАНИЕ ЭЛЕКТРОПОТРЕБЛЕНИЯ В РЕГИОНАЛЬНОЙ ЭНЕРГОСИСТЕМЕ С УЧЕТОМ РЕЖИМА ЕЕ ЭКСПЛУАТАЦИИ

The article is devoted to the research of the assessment of influencing factors and forecastingof power consumption in the regional power system, taking into account its operating modes.The analysis of existing methods of forecasting energy consumption is carried out. The choice of aforecasting method using an artificial neural network is justified. An algorithm for creating a neuralnetwork for short-term prediction of electrical load is considered. The relevance of the work isdue to the requirements of the current legislation for forecasting electricity consumption in orderto solve the problem of maintaining a balance of power between the generating side and the consumptionof electric energy. At the same time, one of the main tasks related to the generation ofelectric energy and its consumption is the task of maintaining a balance of capacities. On the onehand, with an increase in the planned load, interruptions in the supply of electricity may occur, onthe other hand, a decrease in electricity consumption will also lead to a decrease in the efficiencyof power plants, and ultimately to an increase in the cost of electricity both for the wholesale electricitymarket and for the end user. The developed neural network model reduces the task of shorttermforecasting of power consumption to the search for a matrix of free coefficients by trainingon available statistical data (active and re-active power, ambient temperature, date and index ofthe day). The received NS model of short-term forecasting of power consumption of a section ofthe district 10 kV electric grid takes into account the factors: – time, - meteorological conditions,– disconnections of individual power supply lines of cottages, – operating mode of electricity consumers.Predictive estimates of the power consumption of the power system have been obtainedbased on the data of the electricity consumed by the outdoor temperature, the type of day, etc. Themodel for predicting the magnitude of the consumed active and reactive power is quite workable,but at this stage still has a fairly high level of forecasting error. To improve the accuracy of forecasting,it is necessary to increase the database that makes up the training sample, because at themoment the available data cover a time period of only 3–4 months. The results of the analysisshowed that forecasting reactive power consumption causes the greatest difficulties.

Feasibility of Replacing Nuclear and Fossil Fuel Energy with Offshore Wind Energy: A Case for Taiwan

Adequate recognition of the offshore wind energy potential may help coastal states frame proper energy policies for replacing nuclear and fossil fuel energy. In this study, we examined the application potential of the offshore wind energy generated by 31 offshore wind farms designated by the Taiwanese government for future exploitation. Our findings indicate that offshore wind energy (through its substantial power generation volume and capacity factor) can play the most pivotal role in future power generation for Taiwan. A total of 59.3 TWh of electricity produced from offshore wind energy and solar photovoltaics (PVs) each year could replace the power generated from nuclear energy by 2025. Coal-fired power generation could be replaced by offshore wind energy and other renewables by 2032. The full exploitation of offshore wind farms as detailed in this study (103.4 TWh/year), together with other renewables, could reduce the share of liquefied natural gas-fired power generation to 5.6% of the total Taiwanese power supply by 2040. Realizing the ultimate target of 100% carbon-neutral power generation would rely mainly on a further decrease in electricity consumption per unit of gross domestic product and the expansion of offshore wind energy and geothermal energy.

The Use of Solar Water Heaters in Iraq An Economic Study

The Iraqi electrical system suffers from an increased electrical load for the available generation, which leads to the separation of a large number of consumers, especially during the peak load period. The water heating load represents a great value, so reducing water heating consumption can reduce the cut-off times. Solar water heaters possess many benefits. Therefore, they are used in many countries in the world today. The climate of Iraq is mostly hot and sunny. This makes using a solar water heater is convenient for consumers, especially in urban areas. For studying the use of solar water heaters in the residential sector, three consumers were selected in Mosul city in northern Iraq. Each consumer differs from the others by the value of their consumption of electrical energy (high, medium, and low). Electricity tariffs for the residential sector in Iraq vary, depending on consumption. The information for the three consumers was obtained from a previous questionnaire. In addition to obtaining their needs for hot water, the results of using solar water heaters on the roofs of the three houses of consumers showed a decrease in electricity consumption and a noticeable saving in the electricity bill. The payback period of the solar water heater system was calculated for each consumer. Reducing consumption reduces the gap between generation and load, resulting in reduced programmed shedding times for consumers. For these benefits and environmental features, it is necessary to generalize the use of solar water heaters in the residential sector in Iraq.

COMPLEX ESTIMATION OF THE POWER CONSUMPTION EFFICIENCY IN INDUSTRY

The peculiarities of mathematical models’ application of electricity consumption for estimation of energy use efficiency in enterprises which provides operation of energy efficiency control systems by comparing actual energy consumption with planned ones are analyzed. Differentiated unit consumption rates were calculated by the chief power engineer's department of the enterprise based on calculated data received from the energy services of the divisions. These rates were used to identify factors that affect electricity consumption. At the same time, the existing equipment in the workshop, its capacity, load and working time were taken into account to fulfill the given production plan, including account repairs, maintenance and the implementation of energy-saving measures. It is determined that in the analysis of energy consumption by individual divisions of the enterprise for each factor enterprises do not always take into account the interconnection of processes taking place in different departments, and as a result, the effectiveness of using mathematical models for both forecasting electricity consumption and assessing consumption efficiency is reduced. Taking into account the factors that are considered as important in determining the electricity consumption of individual units according to the observations of the Department of Chief Energy (taking into account their mutual influence), the regression equation was found and evaluated. It was established that the use of refined mathematical models with a narrowed confidence interval expands the potential for energy saving of the enterprise and prompts a more detailed analysis, the search for additional controlled and uncontrolled factors of influence on the efficiency of electricity consumption. 
 An analysis of mathematical models of power consumption of the catching workshop and the boiler turbine workshop as the main consumers showed that the factors affecting electricity consumption are uncontrolled. By changing them it is possible to achieve a decrease in electricity consumption, but not an increase in its efficiency. The main disadvantage of mathematical models of electricity consumption used in the enterprise to forecast the volume of electricity consumption and estimate the efficiency of energy use is their additivity for various departments. It resulted in a regression model of electricity consumption to estimate the relationship between the energy consumption value and their defining parameters (coke production, coke gas, steam production, electricity). The estimation of the electricity consumption model, using the coefficient of determination, has done.

Do COVID-19 Lock-Downs Affect Business Cycle? Analysis Using Energy Consumption Cycle Clock for Selected European Countries

On 11 March 2020, the WHO declared the COVID-19 epidemic to be a global pandemic. This was a consequence of the rapid increase in the number of people with positive test results, the increase in deaths due to COVID-19, and the lack of pharmaceutical drugs. Governments introduced national lockdowns, which have impacted both energy consumption and economies. The purpose of this paper is to answer the following question: do COVID-19 lockdowns affect the business cycle? We used the cycle clock approach to assess the magnitude of decrease in electricity consumption in the three waves of the epidemic, namely, April 2020, November 2021, and April 2021. Additionally, we checked the relation between energy consumption and GDP by means of spectral analysis. Results for selected 28 European countries confirm an impact of the introduced non-pharmaceutical interventions on both energy consumption and business cycle. The reduction of restrictions in subsequent pandemic waves increased electricity consumption, which suggests movement out of the economic recession.

Optimization of Energy Consumption in the Pumping Station Supplying Two Zones of the Water Supply System

Water supply pumping stations are among the main energy-consuming elements in the water supply system. The energy optimization of a pumping station can significantly affect the energy consumption of a water utility. This article deals with the energy optimization of water pumping stations. The work assumes several variants of optimization of water supply pumping stations through changes in the water supply system, pressure changes in the pumping station, and modification of the number of pumps. After analyzing the network, conducting field tests, and creating a model of the water supply network, the network was calibrated in order to reproduce the existing water network as accurately as possible. Then, a variant analysis was performed, and the best optimization method for the pumping station was selected. In two variants, there was a decrease in electricity consumption; in three there, was an increase; in one, there was no change. By connecting the DMA zones and modifying the pressure in the pumping station, the energy consumption of the pumping stations was reduced. On this basis, it was found that it is possible to optimize the water pumping station by modifying the pumping station and work related to the network layout.

COVID-19 Pandemic Effect on Energy Consumption in State Universities: Michoacan, Mexico Case Study

The COVID-19 pandemic has generated multiple impacts. In particular, in the educational sector, the virtual class modality generated changes in the patterns of energy consumption at the institutional level; the identification of this consumption will allow us to reflect on new energy saving and efficient use strategies. In this research, we present a case study of the effects of the COVID-19 pandemic on electricity consumption in 13 state universities in Michoacán, Mexico. Electric energy consumption has been evaluated before and during the presence of the COVID-19 between 2019 and 2020. The comparative analysis estimated the reduction in energy consumption and its economic and environmental impact. The results show a considerable decrease in electricity consumption, generating an average saving of 76.24 MWh/month, which translates into an annual emission reduction from 2019 to 2020 of approximately 497 TnCO2e, and in economic terms of $8,882.25 USD each month. In general, it was identified that consumption patterns in the use of machinery and computer equipment for administrative activities were drastically reduced. If education continues in virtual or hybrid modes, energy consumption schemes will continue to decline and institutions could move towards resilient, affordable, and sustainable models of energy production and consumption.