Annual Electricity Consumption Research Articles

Assessing the Theoretical, Minimal Intervention Potential of Floating Solar in Greece: A Policy-Oriented Planning Exercise on Lentic Water Systems of the Greek Mainland

According to the recent revision of the Greek National Energy and Climate Plan, the country sets out to accomplish an ambitious target concerning the integration of renewables in the local electricity mix during the ongoing decade, at the levels of 80% by 2030. This implies the need to more than double the existing wind and PV capacity at the national level, which in turn introduces numerous challenges. Amongst them, spatial planning for new RES installations seems to be the most demanding, with the adoption of novel technological solutions in the field of RES potentially holding a key role. New technologies, like offshore wind and floating solar, are gradually gaining maturity and may offer such an alternative, challenged at the same time however by the need to entail minimum disruption for local ecosystems. To that end, we currently assess the theoretical potential of floating PVs for lentic water systems of the Greek mainland. We do so by looking into 53 different lentic water systems across the Greek territory that meet the constraint of 1 km2 minimum surface area, and we proceed with the estimation of the relevant floating PV capacity per system under the application of a minimal intervention approach, assuming PV coverage of 1% over the total lentic water system area. In this context, our findings indicate a maximum, aggregate theoretical capacity that could exceed 2 GWp at the national level, with the respective annual energy yield reaching approximately 4 TWh or, equivalently, >6% of the country’s anticipated annual electricity consumption in 2030. Finally, our results extend further, offering a regional level analysis and a set of policy directions and considerations on the development of floating solar in Greece, while also designating the energy merits of floating PVs against similar, land-based installations.

Geothermal Heat Pump for Space Cooling and Heating in Kuwaiti Climate

Kuwait stands as one of the hottest locations globally, experiencing scorching temperatures that can soar to 50 °C during the summer months. Conversely, in the winter months of December and January, temperatures may plummet to less than 10 °C. Maintaining a comfortable temperature indoors necessitates a substantial amount of energy, particularly during the scorching summer seasons. In Kuwait, most of the electrical energy required for functions such as air conditioning and lighting is derived from fossil fuel resources, contributing to escalating air pollution and global warming. To reduce dependence on conventional energy sources for heating and cooling, this article presents a case study to explore the potential of using geothermal energy for space heating and cooling in Kuwait. The case study involves utilizing a geothermal heat pump (water-sourced heat pump) in conjunction with a vertical-borehole ground heat exchanger (VBGHE). The mentioned system is deployed to regulate the climate in a six-floor apartment block comprising a small two-bedroom apartment on each level, each with a total floor area of 57 m2. Two geothermal heat pumps, each with a cooling capacity of 2.58 kW and a heating capacity of 2.90 kW, connected to two vertical-borehole heat exchangers, were deployed for each apartment to maintain temperatures at 22 °C in winter and 26 °C in summer. The findings indicate that the estimated annual energy loads for cooling and heating for the apartment block are 42,758 kWh and 113 kWh, respectively. The corresponding electrical energy consumption amounted to 9294 kWh for space cooling and 113 kWh for space heating. The observed peak cooling load was approximately 9300 kJ/h (2.58 kW) per apartment, resulting in a power density of 45 W/m2. Moreover, the HP system achieved a 22% reduction in annual electric energy consumption compared to conventional air conditioning systems. This reduction in electric energy usage led to an annual CO2 reduction of 6.6 kg/m2.

Mathematical modeling of minimization of electricity consumption by industrial enterprises with continuous production

Relevance. Determined by the importance of minimizing electrical power consumption in industrial enterprises with continuous production, considering the specific characteristics of their technological processes and the requirements to maintain the output volume of their products. Aim. To solve the task of minimizing electrical power consumption based on a mathematical model and gradient method under optimal planning of the production volume of the industrial enterprises with continuous production; to develop a mathematical model for optimal distribution of production over a time cycle (month, quarter, year) across departments, taking into account both simple and functional constraints, derived from the condition of ensuring minimal electrical power consumption in industrial enterprises with continuous production. Methods. When developing the mathematical model for ensuring minimal electrical power consumption while preserving the production volume, classic Lagrange optimization methods were used. To ensure sufficient calculation accuracy, iterative methods were also applied. For the task under consideration, a calculation error margin of ε=0,1 was assumed and established. It is known that the choice of calculation error margin depends on the specifics of the problem at hand and the decision-maker. To verify the adequacy of the developed model, the method of finding the relative extremum of a function of several variables was used. Results. The use of the mathematical model, which takes into account the nature of the technological process and boundary conditions in both simple and integral forms, demonstrated the feasibility of optimal planning of electrical power consumption by the enterprise. The effectiveness of the developed approaches was verified using a metallurgical enterprise as an example of an industrial enterprise with continuous production, in solving the task of minimizing electrical power consumption for products produced during the reporting period. The use of the proposed model allowed for a reduction in annual electrical power consumption by 2.5% while maintaining the same production volume. One of the classic optimization methods – the method of finding the relative extremum of functions of several variables – showed almost identical results upon verification. This serves as further evidence of the adequacy of the proposed model.

Appraising the Feasibility of 3D Printing Construction in New Zealand Housing

The construction industry in New Zealand is significantly impacted by the importance of housing, particularly as urbanisation continues to grow in major cities. Modern construction methods, such as offsite construction and building automation, evolving into digital manufacturing and construction in the industry, have become prominent. Despite the global recognition of 3D printing technology, its adoption in the construction industry in New Zealand is still relatively limited. This study aims to examine the feasibility of 3D printing construction in response to current market challenges, innovation, and the 2050 net-zero carbon goal. Utilising Building Information Modelling (BIM) and Life Cycle Assessment (LCA) approaches, this study investigated the environmental impacts of three housing types: 3D printing (3DP), light steel framed (LSF), and timber. This study used cradle-to-cradle as the system boundary. The results indicate that the 3DP house emits 20% fewer carbon emissions than the traditional timber house and 25% less than the LSF house. Additionally, the 3DP house exhibits a 19% lower annual electric energy consumption than the timber house. Therefore, in response to the growing housing demand in New Zealand, the construction industry must innovate and embrace digital and advanced construction methods, including the adoption of 3D printing.

Current inequality and future potential of US urban tree cover for reducing heat-related health impacts

Excessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO2e yr−1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO2e yr−1. The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceed these benefits. The exception is areas that currently have less tree cover, often the majority POC, which tend to have a relatively high return on investment from tree planting.

Fostering Sustainability through the Integration of Renewable Energy in an Agricultural Hydroponic Greenhouse

This research explores the feasibility of integrating renewable energy sources, such as solar and wind, to power a hydroponic greenhouse. In this way, the latter’s energy autonomy is ensured. The study begins by evaluating the annual electricity consumption of the examined system. A renewable energy system capable of meeting its energy requirements throughout the year is also designed. The main objective is to assess the efficiency of two types of renewable energy sources, namely photovoltaic panels and wind turbines, and to improve their integration within the agricultural chamber by implementing a model simulation. Two scenarios were examined: the first one represents a photovoltaic power plant with storage, connected to the grid, while the second scenario presents a wind power plant connected to the grid. This numerical analysis is supplemented by a one-year experimental study of a photovoltaic installation connected to the network with storage, which in turn is connected to the experimental device. To handle energy within the renewable energy greenhouse, an energy management system was developed based on a fuzzy logic controller. This system aims to maintain energy balance and ensure continuous power supply. The energy management system optimizes energy flow to minimize consumption, reduce grid dependence, and improve overall system efficiency, resulting in cost savings and certain environmental benefits.

Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

Harvesting sustainability: vertical agricultural greenhouses powered by renewable energy technologies

ABSTRACT This study investigates the practicality of integrating renewable energy systems to meet the electricity needs of a self-contained hydroponic greenhouse that functions as an environmentally friendly facility with reliable air conditioning and electrical infrastructure. The first step is to assess the greenhouse’s annual electricity consumption and design a renewable energy system that can sustain this year-round. The results show that daily electricity consumption is between 0.75 and 2 kWh, with thermal air conditioning accounting for over 50% of the total consumption. To address this issue, a simulation model is developed to evaluate the performance of a hybrid photovoltaic/wind system connected to an electric storage battery and to optimize its integration into the greenhouse. This causes the battery’s voltage to generally increase during charging (to 350.5 V) and its state of charge to change non-linearly depending on the battery’s chemical makeup. An Energy Management System (EMS) is then developed for the Hybrid Renewable Energy System (HRES), which works in two modes to ensure the energy balance for uninterrupted power supply. An economic and environmental analysis of three scenarios is developed and concludes that the systems are effective and sustainable, with an estimated payback period of approximately 6 to 8 years.

Innovative retrofitting approaches for energy saving in Saudi public schools

Saudi buildings use approximately 29% of the primary energy and about 80% of the electrical energy. Because of the hot and dry climate, air conditioners consume a lot of electrical energy to attain a comfortable temperature indoors. More than 70% of Saudi buildings need to be thermally insulated. The objective of this study is to assess the existing energy saving of public schools in Saudi Arabia. Indoor thermal environment monitoring of three school buildings has been undertaken by employing LSI LASTEM ‘R-LOG’ data loggers, and electric energy bills of the schools were also collected. The school building was modelled using DesignBuilder software, which was utilized to investigate the impact of various modifications made to the building envelopes, including changes to building orientation, the inclusion of thermal insulation layers, various types of glass and shading devices. According to the findings, implementing appropriate building envelope design techniques for existing schools can lead to significant reductions in cooling and overall energy consumption, with savings of 30% and 19%, respectively. After applying these strategies collectively, the school managed to reduce its total annual electricity consumption by nearly 19.2%, resulting in a decrease from 279.13 MWh to 225.5 MWh per year.

Examining the magnitude and causes of variation in electricity use in a sample of Indian dwellings with and without air conditioning

ABSTRACT Understanding the factors that drive the use of residential electricity in India is essential for designing policies that can reduce its expected growth. This study empirically examines the magnitude and causes of variation in electricity use in a sample of Indian dwellings with and without air conditioning (AC), using an interview-based survey approach applied in 41 dwellings located across two cities representing the composite climate of India. Statistical analysis was used to unpack the relationship between electricity use and socio-technical factors. Despite the small sample size and having the same climatological realm, there was a wide variation observed in electricity use by income groups and the presence of ACs. The mean annual residential electricity consumption (REC) was observed to be highest in high-income group dwellings (5,618 kWh/year), followed by middle-income group dwellings (3,870 kWh/year) and lowest in low-income group dwellings (2,169 kWh/year). The mean REC of AC dwellings (4,208 kWh/year) was found to be nearly double that of non-AC dwellings (2,260 kWh/year) with significant seasonal variation. Multivariate regression analysis revealed that the presence of ACs, household size (number of occupants), annual non-AC appliance hours, dwelling size (number of rooms) and income group accounted for 80% of the variability in REC across the study sample. Residential energy policy in India should consider these key factors that drive REC. To be effective, such policy programmes need to be customised for different income groups, adopting incentives as well as energy awareness campaigns to influence both purchasing and habitual behaviours.

Permanent VS Transitory Shocks to Electricity Consumption: Panel Evidence from 19 African Countries

ABSTRACT: Considering Africa's increasing electricity demands and the pivotal role of electricity in socio-economic development, this study addresses a research gap in understanding the stochastic properties of per capita electricity consumption in African contexts. Through examining the integration properties of per capita electricity consumption across 19 African countries, the research determines whether 'shocks' yield transitory or permanent effects, aiding policy formulation and future demand forecasting. Annual electric power consumption data (1971 – 2014) (kWh per capita) from the WDI across 19 African countries are used. Our methodology starts with formally testing for cross-sectional dependence (CSD) using various tests to avoid potential size distortions. Subsequently, we employ the novel panel stationarity test of Nazlioglu and Karul (2017), a Fourier KPSS panel stationarity test. The robustness of the analysis is enriched by incorporating the Hadri and Kurozumi (2011) panel stationarity test and Nazlioglu et al. (2021) new panel stationary tests. The CSD test results reveal mutual dependency in electricity consumption among the sampled African countries, underscoring the need for a panel testing methodology that accommodates CSD. The results of the panel stationarity tests that allow CSD and structural breaks—specifically, the Nazlioglu and Karul (2017) test, which models structural breaks as a gradual process—reject the null hypothesis of trend stationarity for both single frequencies, i.e., k =1, and 2. Similarly, all new, complementary panel stationarity tests from Nazlioglu et al. (2021) reject the null hypothesis of stationarity for both frequencies. Collectively, the outcomes from various panel stationarity tests present strong evidence of nonstationary per capita electricity consumption within the sampled African countries. Additionally, the Nazlioglu and Karul panel stationarity test enables an analysis of the individual Fourier KPSS test for cross-sectional units in the panel. Of the 19 countries, only Togo does not reject the null hypothesis of stationarity for both frequencies. These individual country results align with the panel outcomes, providing compelling evidence for the nonstationarity of the per capita electricity consumption series across the investigated African nations. Accordingly, shocks to electricity consumption will have permanent effects, indicating that per capita electricity consumption will not return to its pre-shock equilibrium path. In this case, demand or consumption stabilizing policies will have long-run effects.

Electricity consumption of anesthesia workstations and potential emission savings by avoiding standby.

Anesthesiology has arelevant carbon footprint, mainly due to volatile anesthetics (scope 1emissions). Additionally, energy used in the operating theater (scope 2emissions) contributes to anesthesia-related greenhouse gas (GHG) emissions. Optimizing the electricity use of medical devices might reduce both GHG emissions and costs might hold potential to reduce anaesthesia-related GHG-emissions and costs. We analyzed the electricity consumption of six different anesthesia workstations, calculated their GHG emissions and electricity costs and investigated the potential to reduce emissions and cost by using the devices in amore efficient way. Power consumption (active power in watt, W) was measured with the devices off, in standby mode, or fully on with the measuring instrument SecuLife ST. Devices studied were: Dräger Primus, Löwenstein Medical LeonPlus, Getinge FlowC, Getinge FlowE, GE Carestation 750 and GE Aisys. Calculations of GHG emissions were made with different emission factors, ranging from very low (0.09 kg CO2-equivalent/kWh) to very high (0.660 kg CO2-equivalent/kWh). Calculations of electricity cost were made assuming aprice of 0.25 € per kWh. Power consumption during operation varied from 58 W (GE CareStation 750) to 136 W (Dräger Primus). In standby, the devices consumed between 88% and 93% of the electricity needed during use. The annual electricity consumption to run 96devices in alarge clinical department ranges between 45and 105 Megawatt-hours (MWh) when the devices are left in standby during off hours. If 80% of the devices are switched off during off hours, between 20and 46 MWh can be saved per year in asingle institution. At the average emission factor of our hospital, this electricity saving corresponds to areduction of GHG emissions between 8.5and 19.8 tons CO2-equivalent. At the assumed prices, acost reduction between 5000 € and 11,600 € could be achieved by this intervention. The power consumption varies considerably between the different types of anesthesia workstations. All devices exhibit ahigh electricity consumption in standby mode. Avoiding standby mode during off hours can save energy and thus GHG emissions and cost. The reductions in GHG emissions and electricity cost that can be achieved with this intervention in alarge anesthesiology department are modest. Compared with GHG emissions generated by volatile anesthetics, particularly desflurane, optimization of electricity consumption of anesthesia workstations holds amuch smaller potential to reduce the carbon footprint of anesthesia; however, as switching off anesthesia workstations overnight is relatively effortless, this behavioral change should be encouraged from both an ecological and economical point of view.

Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective

Taiwan’s power system operates as an isolated grid, preventing the export of surplus energy. Excess electricity is either stored or discarded (curtailed). This study aims to estimate the energy storage requirement for the day with the most extreme electricity consumption behavior in a year without energy curtailment. Based on the installed capacity and actual power generation of renewable energy sources in 2022, this research estimates the power generation per GW of the installed capacity at full load. Integrating the government’s annual installation capacity plans, this study forecasts the annual power generation of renewable sources. Using the electrical load during the 2023 Spring Festival as a baseline and assuming an annual electricity consumption growth rate of 2%, combined with a minimum gas power output of approximately 6 GW, this study calculates the unused power generation, which represents the pumped-storage hydroelectricity and battery energy storage systems requirements for that day under a no-curtailment scenario. Considering the semi-annual adjustments in installation planning, this study’s code is open-sourced and designed to provide updated results with new planning data input, facilitating ongoing adjustments.

Modelling and forecasting India's electricity consumption using artificial neural networks

AbstractPrecise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand.

Power Control System Design for Monitoring Active and Standby Power Consumptions of PC Labs

In this study, it was aimed to provide energy saving by informing the user about the power consumption instantly with the power control system (PCS) design and by cutting the energy of the devices in standby mode. The power consumptions of 39 PCs in Lab-1 and Lab-2 in Amasya University Engineering Faculty were examined through the designed system. Lab-1 contains older PCs while Lab-2 contains new generation PCs. The PCs used in the measurements made within the scope of the study were energized via PCS instead of a direct electrical socket. With this method, it has been tried to determine the electrical power consumptions in active work and standby work for calculate the annual electricity consumption and to reflect it on the invoices. The experimental method was carried out on the daily PC staying active for 2 hours and standby for 22 hours. According to the results, it is predicted that 56% lost standby power on an annual basis can be avoided with the use of PCS. The annual invoice cost of this lost power is calculated as 14.116,93 TL and 8.470,12 TL for Lab-1 and Lab-2 respectively. It is thought that the study will make an important contribution to researchers working on this subject.

Climate Change Implications for Optimal Sizing of Residential Rooftop Solar Photovoltaic Systems in Qatar

Climate change poses critical challenges for Qatar’s energy-intensive residential building sector. This study evaluates the impact of projected climate warming on optimizing rooftop solar photovoltaics (PV) for villas. An integrated modelling approach is employed, combining building energy simulation, PV system optimization, and performance assessment under varying climate scenarios. A typical Qatari villa is modelled in DesignBuilder and simulated under the baseline (2002) conditions and the projected years 2016, 2050, and 2100, reflecting incremental warming. Results show the villa’s annual electricity consumption will grow 22% by 2100, with summer peaks escalating to 26% driven by surging cooling demands. Techno-economic optimization in HOMER Pro (version 3.10) verifies a grid-connected rooftop PV system as optimal in all years, with capacity expanding from 7.4 kW to 8.2 kW between 2002 and 2100 to meet rising air conditioning loads. However, as temperatures increase, PV’s energy contribution declines slightly from 18% to 16% due to climate change degrading solar yields. Nonetheless, the modelled PV system maintains strong financial viability, achieving 5–8 years of paybacks across scenarios. This analysis provides empirical evidence of distributed PV’s effectiveness for Qatar’s households amidst escalating cooling consumption. However, maintaining solar mitigation potential requires evolving PV sizing methodologies and incentives to account for declining panel productivity at the country’s peak temperatures exceeding 50 °C. Overall, this study’s integrated framework evaluates residential solar PV systems’ capabilities and appropriate policy evolution under projected climate impacts for the first time in Qatar. The modelling approach and conclusions can inform building codes and pro-solar policies to accelerate adoption for emissions reduction. With villas representing over 100,000 units in Qatar, widespread rooftop PV integration can meaningfully contribute to national sustainability targets if implementation barriers are addressed considering climate change effects.

Modelling of Automated Store Energy Consumption

Over the last decade, a constantly growing trend of the popularization of self-service automated stores has been observed. Vending machines have been expanded into fully automated stores, the offer of which is comparable to small, conventional stores. One of the basic reasons for the popularization of modern automated stores is the reduction in a store’s energy consumption while ensuring a comparable range of products offered. The research into possibilities of reducing greenhouse gases emission is important in terms of the environment and climate protection. The research presented in the paper concerns the development of a model for determining electricity consumption, operating costs and CO2 emission of an automated store designed and developed by Delfin company. In the developed model, the potential location of the store, prevailing climatic conditions and expected product sales are taken into account. Estimated energy demand for the store is the information of key importance for the potential investors and the manufacturer of the automated store. It is worth emphasizing that the average annual electrical energy consumption evaluated for a grocery store of an area of 70 m2 amounted to approximately 38.4 MWh, while for an automated store of an area of 9 m2 and a comparable product range, the electricity consumption was approximately 10.1 MWh, i.e., 74% smaller.

The ANALYSIS OF ELECTRICITY CONSUMPTION OF A STORE FACILITY

The paper presents the balance of electricity and fuel oil consumption of a medium-sized 24-hour store. Measurements of electricity consumption of individual components of the balance were made. Their shares over the 12 months of measurements were determined. Opportunities to reduce electricity consumption by changing the type of lighting of the facility were identified. . lighting replacement reduced electricity consumption by 38535.8 kWh, a total reduction of 21.2% in the store's annual electricity consumption.

Weka Modeli Kullanılarak Libya Elektrik Şirketinin Kayıp Enerji Verilerinde Enerji Tassarufu İçin Sınıflandırma Algoritmalarının İyileştirilmesi

The main goal of this study is to compare the performance of the classification algorithms applied to the SCADA database of the Supervisory Control and Data Acquisition (SCADA) system of the General Electricity Company of Libya (GECOL). The company's annual energy and material losses have become seriously important to the Libyan government's research field. The well-established data mining and classification software package known as the WEKA tool is used to minimize these losses,. As necessary data input for algorithms; six different parameters are taken into consideration, namely power production size, energy production duration, energy production date, ambient temperature, humidity level and wind speed. This study is examined in detail for the first time in this article. In addition, considering the temperature variables, humidity, wind and other atmospheric effects of the environment, the energy losses of the company and the country are reduced to a minimum level. As a result, the company's annual electricity consumption is classified as low, medium or high consumption with the simulations. Thus, in cases where energy consumption is high, it is possible to make accurate and rapid decisions regarding the determination and classification of time periods related to energy consumption.

Study on the Change of Power Consumption Structure in Xumai Township, Nimu County, Lhasa, Xizang

The research on the evolution of rural power consumption structure in Xizang reflects the information on rural power development in Xizang, and the information on rural power development shows the people's living standards and demand trends. In order to understand the depth of changes in the power consumption structure of farmers and herdsmen households, the project team conducted a field survey of the changes in power supply sources, power consumption structure, power consumption equipment, and operation and management status of Sumai Township, a typical village in Nimu County, Lhasa City, Xizang over the years, and explored the history and trend of power development in remote villages in Xizang. Field research has found that before 2014, the township mainly relied on small-scale independent solar photovoltaic power supply, which was almost used for lighting; Since the implementation of the State Grid in 2014, electricity consumption has shifted from low voltage to wide voltage. In the past three years, the annual electricity consumption growth rate in Xumai Township has exceeded 6%, and the power supply stability has reached 90%. At the same time, the township utilizes solar photovoltaic street lights. The electrical appliances used have developed from a single lamp to power appliances such as household appliances, agricultural electric vehicles, tsamba machines and oil presses. The distribution line of the township is gradually being replaced with medium and low voltage, and is equipped with a 35kV Xumai substation. The 10kV medium and low voltage distribution network project of the rural power grid is gradually being put into operation and put into practice. However, research has found that villagers hope to use more clean energy such as solar energy for power supply and heating, in order to reduce the cost of electricity and gas bills.