Estimation Of Electricity Consumption Research Articles

Remote sensing of night‐time lights and electricity consumption: A systematic literature review and meta‐analysis

AbstractNight‐time light (NTL) satellite imagery can provide unique insights into the energy sector. Nevertheless, there are limited studies that have systematically reviewed the literature on the relationship between electricity consumption and NTL. Therefore, this paper aims to provide a systematic review of studies that have explored this relationship. The review identified over 200 regression models estimating electricity consumption using NTL satellite images. The key finding of the review was that there was a large variability in regression performance for model prediction of electricity consumption from NTL imagery, indicating a need for further work to refine the techniques and approaches in this emerging field of remote sensing research. The level of spatial aggregation had an important influence on model performance with larger geographical areas, such as countries or states, providing better estimations.

An extended approach to appraise electricity distribution and carbon footprint of bitcoin in a smart city.

A nation cannot sustain a highly productive and efficient population without smart cities. Due to their significant reliance on digital technologies, these cities require a high level of cybercrime protection. Cryptocurrencies have gained significant attention due to their secure and reliable infrastructure. The decentralised cryptocurrency operates in a trust-less environment known as the blockchain, where each network participant has a ledger copy of all transactions. Blockchain technology employs a proven consensus mechanism without requiring establishment of a central authority. But the consensus mechanism requires miner to solve a cryptographic problem by generating random hashes until one of them matches the desired one. This procedure is energy-intensive, and when thousands of miners repeat it to verify a single transaction, a substantial amount of electricity is consumed. Moreover, electricity produces a significant amount of carbon footprint. Patch methodology utilises the data of all hashes created per year and the efficiency of mining hardware over a 10-year period to calculate the Bitcoins energy consumption. Due to a large number of unknown and uncertain factors involved, it is difficult to precisely calculate a single value for electricity consumption and carbon footprint as reported by Patch methodology. The proposed method extends the Patch methodology by adding a maximum and minimum limit to the hardware efficiency as well as the sources of power generation, which can help refine estimates of electricity consumption and carbon emissions for a more accurate picture. Using the proposed methodology, it was estimated that Bitcoin consumed between 38.495 and 120.72 terawatt hours of electricity in 2021 and released between 2.12 and 45.37 million metric tonnes of carbon dioxide. To address the issue of excessive energy consumption and carbon emissions, a significant number of individual miners and mining pools are relocating to energy-intensive regions, such as aluminium mining sites that rely on hydroelectricity for energy generation.

Application of The Combined Method in Inventory Forecasting Electricity at PT PLN (Persero) ULP Sibuhuan

The forecast of electricity consumption is a prediction of the use of electricity for the future by referring to the use of electricity in past data. Estimates of electricity consumption needs are intended to estimate how much electricity consumption is used by customers and must be provided by PT PLN (PERSERO) ULP SIBUHUAN as a provider of electrical energy services. Population growth occurs along with the development of an area, so it affects the demand for electricity and the need for electricity consumption. Load mapping must be done to maintain the continuity and distribution of electrical energy to customers. One way to preserve the continuity of service is to estimate electric power consumption using several forecasting methods. One of the methods that can be used is to combine several load forecasting methods called the combined method. The combined method is a model that incorporates various estimation methods, including econometrics, analytics, and trends, using historical customer data originating from BPS (Central Statistics Agency) Padang Lawas Regency and PT. PLN (PERSERO) ULP SIBUHUAN. The estimated load between 2021 and 2025 is 625,070,452 kWh, with an increase in electricity consumption of 20.7%, household expenses increasing by 1.8%, and business sector expenses by 1.35%. The method used is to calculate manually using Microsoft Excel to obtain forecasting values for population growth; increasing electricity consumption is very important for planning electric power generation, development, and planning of electric power distribution and mapping of loads on customers.

Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

The study of the quality of multi-step time series forecasting

The work is devoted to the study of the quality of multistep forecasting of time series using the electricity consumption data for forecasting. Five models of multistep forecasting have been implemented, with their subsequent training and evaluation of the results obtained. The dataset is an upgraded minute-by-minute measurement of four years of electricity consumption. The dataset has been divided into training, validation, and test samples for training and testing models. The implementation is simplified by using the TensorFlow machine learning library, which allows us to conveniently process and present data; build and train neural networks. The TensorFlow functionality also provides standard metrics used to assess the accuracy of time series forecasting, which made it possible to evaluate the obtained models for forecasting the time series of electricity consumption and highlight the best ofthose considered according to the given indicators. The models are built in such a way that they can be used in studies of the quality of time series forecasting in various areas of human life. The problem of multistep forecasting for twenty fourhours ahead, considered in the paper, has not yet been solved for estimating electricity consumption. Theobtainedforecasting accuracy is comparable to recently published methods for estimating electricity consumption used in other conditions.At the same time, the forecasting accuracy of the constructed models has been improved in comparison with other methods.

Modelagem bottom-up do consumo por uso final de eletricidade no setor residencial no Brasil

Abstract Electricity consumption in the residential sector in Brazil has been increasing annually despite efforts to promote the energy efficiency of household appliances. One of the main goals for achieving more energy efficiency in dwellings is understanding its energy end uses. In this context, this paper presents a bottom-up model developed to analyse regional and national electricity end uses in the residential sector in Brazil based on a recent survey on Ownership of Appliances and Consumption Habits. The percentages of total electricity consumption associated with nine appliances (light bulbs, refrigerators, freezers, televisions, showers, microwaves, washing machines, fans, and air conditioners) were estimated. The values were obtained using the software EnergyPlus for air conditioners and electricity consumption equations for the other eight appliances. Results show that the proposed model gives reasonable estimates of electricity consumption, which were close to the values expected for most appliances. Regionally, the appliances for which ownership and pattern of use are influenced by the climate (electric showers, fans, and air conditioners) obtained the most significant variation in the percentage of electricity consumption.

Short-mid term electricity consumption prediction using non-intrusive attention-augmented deep learning model

Estimates of electricity consumption (EC) can provide effective guidance for energy allocation and energy-saving measures. For improving the accuracy of short-mid term EC forecasting, a novel nonintrusive attention-augmented deep learning model is proposed. This model, which is referred to as NAP-BiLSTM, consists of a nonintrusive attention-augmented-based preprocessing (NAP) block and a regular bidirectional long short-term memory (BiLSTM). The NAP block is a kind of plug-and-play attention mechanism for time series, which can be combined with a deep neural model that processes sequential data without modifying its structure. The effectiveness of the proposed model is verified by two experiments, including univariate prediction using EC data from the U.S. and multivariate prediction using weather and energy data from Valencia. The root mean square error, mean absolute error, and mean absolute percentage error are used as metrics. The results demonstrate that the proposed model is highly promising for both univariate and multivariate analyses and outperforms state-of-the-art deep learning models.

The Use of Regression Method on Simple E for Estimating Electrical Energy Consumption

The continuous increase in population growth has an impact on the electrical energy supply. Based on this increase, electric power producers serve customers using proper forecasts. Therefore, it is a necessity to select the right calculation method with easy implementation. In this study, the population forecasts and economic growth calculations using the GT (Growth Trend) regression method development on Simple E were obtained for the year 2028. Furthermore, electricity consumption estimation was carried out using the DL (Double Log) regression method with growth trend, R, AR, DW, and t values of 6.63%, 0.993, 0.992, 1.21, and 2.18, respectively. The results show that estimated energy consumption was 6.63% annually, with the achievable amount for 2028 being 19,839.83 GWh. Doi: 10.28991/HIJ-SP2022-03-06 Full Text: PDF

Construction and Verification of a Model for Estimating Electricity Consumption of Residential Buildings in a Region for the Development of an Area Energy Demand Simulation Tool

Construction and Verification of a Model for Estimating Electricity Consumption of Residential Buildings in a Region for the Development of an Area Energy Demand Simulation Tool

Model of monthly electricity consumption of healthcare buildings based on climatological variables using PCA and linear regression

At this time, due to the global pandemic that has occurred, public administrations want to optimize resources and reduce greenhouse gases with more interest than before. It is the case of the Energy Regional Entity of the Junta de Castilla y León (Spain) that pursues the optimization of the energy consumption in particular of healthcare sector buildings. For this purpose, this work focuses on estimating electricity consumption for each month, for which different scenarios will be generated and the corresponding model is obtained for each scenario. This model has been developed considering the historical monthly data of consumption and climatic variables for the last 3 years. Electricity consumption in public sanitary buildings is related to their climatology, due to the use of air conditioning to adjust the indoor temperature. Subsequently, from the models obtained, the results will be analyzed. Significant differences have been observed in the estimation of electricity consumption with respect to the real data provided by the Junta de Castilla y León. The results obtained show how the availability of climatic variables increases the accuracy of the model obtained by about 30%.

Using Multi-Source Geospatial Information to Reduce the Saturation Problem of DMSP/OLS Nighttime Light Data

The DMSP/OLS Nighttime light (NTL) data directly reflect the spatial distribution and light intensity of artificial lighting from the Earth’s surface at night, and has become an emerging instrument for urbanization research, including in the monitoring of urban expansion, assessment of socio-economic vitality, and estimation of energy consumption and population. However, due to the imperfect sensor design of DMSP/OLS, the dynamic range of the digital number (DN) of NTL is limited (0, 63), leading to a significant saturation problem when describing the actual light intensity, especially in dense urban areas with high light intensity. This saturation problem masks spatial differences in light intensity and weakens the reliability of DMSP/OLS NTL data. Therefore, this study proposes a novel desaturation indicator that combines NDBI and POI, the Building and POI Density-Adjusted Nighttime Light Index (BPANTLI), to regulate the DMSP/OLS NTL saturation problem based on the spatial characteristics of urban structures and human activity intensity. The proposed method is applied to three urban agglomerations with the most severe light saturation issues in China. The geographical detector model is firstly utilized to quantify the effectiveness of NDBI and POI in reflecting the difference in light intensity distribution from the NTL potential saturation region (NTL DN value (53, 63)) and NTL unsaturation region (NTL DN value (0, 52)), so as to clarify the feasibility of developing the BPANTLI. The applicability of BPANTLI is validated through three aspects—comparison of the desaturation capacity and the performance of delineating light intensity; verification of the consistency of BPANTLI with radiometric calibration nighttime light product (RCNTL) and NPP/VIIRS data; and assessing the accuracy of the BPANTLI in estimating socio-economic parameters (GDP, electricity consumption, population density). The results indicate that the BPANTLI possesses superior capability in regulating the NTL saturation problem, achieving good performance in distinguishing inner-urban structures. The regulated results reveal a remarkably improved correspondence with the RCNTL and NPP/VIIRS data, providing a more realistic picture of the light intensity distribution. It is worth noting that, given the advantages of NDBI and POI vector data in spatial resolution, the BPANTLI established in this study can overcome the limitation of the spatial resolution of DMSP/OLS nighttime lighting data and achieve dynamic transformation of the spatial resolution. The higher spatial resolution desaturation results allow for a better characterization of the light intensity distribution. Moreover, the BPANTLI-regulated light intensity significantly improves the accuracy of estimating electricity consumption, GDP, and population density, which provides a valuable reference for urban socio-economic activity assessment. Thus, the BPANTLI proposed in this study can be considered as a reasonable desaturation method with a high application value.

Latent demand for electricity in sub-Saharan Africa: a review

Abstract Universal access to electricity is an essential part of sub-Saharan Africa’s path to development. With the United Nations setting Goal 7 of its sustainable development goals to be universal access to clean, reliable and affordable electricity, substantial research efforts have been made to optimize electricity supply based on projected demand in sub-Saharan African (SSA) countries. Our study reviews the literature on electricity demand, with a specific focus on latent demand (i.e., electricity demand that would exist if the necessary techno-economic conditions were met) in SSA. We found that out of 57 electricity demand papers reviewed, only 3 (5%) incorporated latent demand in their electricity demand projections. Furthermore, majority of the literature on electricity consumption and demand estimation in SSA use econometric models to identify determinants of electricity consumption and project future demand. We find that population density, urbanization, household income, electricity price, market value of crops and availability of natural resources to be significant determinants of electricity consumption in SSA. We conclude the review by proposing a methodology, and providing an initial proof of concept, for more accurately projecting latent demand in sub-Saharan Africa. Incorporating latent demand in electrification models would help inform energy sector stakeholders (e.g., investors and policymakers) about which sectors and geographic locations hold potential for wealth creation via electricity access.

Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images

The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.

Time Series Long-Term Forecasting of per Capita Electricity Consumption for Bangladesh

Bangladesh government has announced Vision-2041 of electricity generation and distribution to uplift the socio-economic conditions of Bangladesh. It is now entering into the list of middle-income countries and now planning for energy as one key measure to sustainable development. Policymakers are trying to forecast the future per capita electricity consumption and set up a feasible way of electricity generation over longer periods for sustainable development of Bangladesh through preventing underestimation or overestimation that could cause a huge loss in the financial sector of Bangladesh. This work focuses on long-term estimation of electricity consumption for Bangladesh, time series models have been used to forecast per capita electricity consumption from fiscal year (FY) 2019/20-2040/41 (next 22 years). An actual past historical data of FY 1976/77-2018/19 (43 years) has been analysed on Minitab 17 to get the most favourable time series model for forecasting per capita electricity consumption of Bangladesh. ARIMA has appeared as the most accurate time series model over the actual historical data of 43 years with the lowest MAPE, MAD, and MSD as 4.50, 3.23, and 15.40, respectively.

Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand

The intensive industrial development in special economic zones, such as Thailand’s Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local electricity consumption when compared to MR and SVR with the lowest error rate and highest R2. The local government in developing countries with limited data and financial resources can adopt the proposed approach to benefit from utilizing commonly available remote sensing and statistical data with simple machine learning models such as DT (regression method) for sustainable electricity management.

Estimation, analysis and mapping of electricity consumption of a regional building stock in a temperate climate in Europe

This study financed by ERDF and the Wallonia Region estimates the annual electricity consumption (EC) of building stock, including the 3 building sectors namely residential, tertiary and industrial building. The estimation takes into account appliances, electrical heating, cooling, lighting, cooking and EC by m2 on a building level. The results are spatialized on different territorial scales. Using cadastral data of more than 1,700,000 Walloon buildings and annual EC data from a sample collected in 2012 from the energy reports, the paper assesses the EC of the whole building stock and tests to what extent different types of variables (building factors and socio-demographics) explain annual EC. It then shows which individual variables have the highest explanatory power. In contrast to many other studies, the research recognizes the problem of multicollinearity between predictors in regression analysis and uses Lasso regression to address this issue. Three separate regression models were used to study the predictors of annual EC of residential, tertiary and industrial buildings. EC building factors (appliances, auxiliary and main heating, domestic hot water and cooking) explained the largest share which is 66.46% of the variability in EC for residential buildings whereas the EC usages share for tertiary buildings (lighting, heating and domestic hot water, air conditioning, cooling, etc.) is about 50.53% and 38.55% for industrial buildings. Socio-demographic variables on their own explained about 61.59%, 26.34% and 3.41% of the annual EC, respectively for residential, tertiary and industrial buildings. Hence, the building variables present the highest explanatory power for EC, presumably because heating and cooling EC are included in this study. The study highlights that when attempting to explain EC related to Walloon households, including heating and cooling EC, appliances usage has the strongest predictive power in residential buildings. On the other hand, the projected decrease in EC use for heating in existing residential buildings is −8.82% and −10% for existing tertiary buildings while the projected increase in EC use for cooling in existing tertiary buildings is + 11.94% from 2012 to 2050 on a regional scale. These trends follow the predicted regional heating degree-days (HDD) of −11.76% and cooling degree-days (CDD) of 14.04% for the same period based on the gated recurrent unit (GRU) an implemented deep learning (DL) model. In addition, the produced EC maps on different territorial scales show that the highest EC is seen in large and main cities in general.

Optimal site selection for wind energy: a case study

ABSTRACT Renewable energy sources (RES) have gained attraction in recent years. The optimal site selection problem is one of the most important problems in the area of RES. In this study, the optimum site selection problem for wind energy, which is one of the RES types, is discussed. Eighty-one provinces in Turkey are evaluated using the Pareto diagram and the provinces with high potential wind energy are obtained. Long-term electricity consumption estimates are performed for the 13 provinces using ARIMA and Time Series Analysis methods. Also, in the article, an integrated optimum site selection problem in GAMS 23.5 software is modeled by the predicted future energy demand. As a result of the study, the total 41 installation decisions are made for the strategic optimal site selection decision between 2021 and 2025 years. Minimizing the total cost proposed as an objective function of the developed mathematical programming model, the total cost is found as 52526500 currency unit. It is seen that the decision to install the power plant is mostly made for Balıkesir, Çanakkale and İzmir, and the number of plants for the provinces are 6, 6 and 5, respectively. The computational results show that the developed model produces effective solutions and minimizes the total cost by focusing on strategic optimal site decisions. According to the sensitivity analysis results, increasing the percentage of use in wind energy approximately 4–5% from 3.5% in total percentage will reduce the total cost by approximately 3.62%.

Rooftop PV: Potential and Impacts in a Complex Territory

When developing a sustainability plan in a complex and heavily urbanized territory, one of the most relevant options available is installing rooftop photovoltaic (PV) panels. Thus, it is essential to determine the amount of available surface and the potential impact of such installations on the energy and emission budget of the area. Instead of processing remotely sensed imagery, which is a long process and does not allow considering the buildings’ ownership, this study develops an approach based on a cluster analysis of the urban/morphological characteristics of the municipalities. Once a clear group diversification is obtained, the roof surface of the center of gravity of each cluster is extrapolated to all similar settlements. This, together with the information of local solar irradiation, allowed us to compute each cluster’s potential solar energy production and its capability to respond to the local energy demand, a key parameter to decide about the possibility of a local smart electricity network. Finally, the emissions avoided thanks to solar PV development are computed in terms of carbon dioxide and other relevant pollutants. This approach is applied to the residential rooftop of Lombardy, a Northern Italy region with a wide variety of urban morphologies and landscapes. The potential production of rooftop PV exceeds the estimated electricity consumption of residential buildings and would allow sparing almost 4 M ton of CO2 equivalent or 5% of the overall regional emissions.

Analysis on electricity use and indoor thermal environment for typical air-conditioning residential buildings in Malaysia

In residential buildings, accurate forecast of time-varying electricity demands is important for the estimation and prediction of electricity consumption. Information of the end user's electricity consumption patterns is necessary as it has a significant impact on the load curve in the utility grid. Thus, this study aims to provide detailed information on the electricity use and indoor thermal environment for typical residential buildings. Field measurements were conducted in 19 selected dwellings in Kuala Lumpur, Malaysia to specifically measure the total and the air conditioner (AC) electricity usage along with the indoor thermal environment. There were significant differences in the total electricity consumption among low-, middle-, and high-income dwellings. However, for the AC electricity consumption, the different income groups do not display notable differences. This might be due to the occupant behaviour of using the air conditioner mostly during sleeping hours. The measurement results of all targeted dwellings showed that the daily averages of the total and the AC electricity consumption are 14.5 kWh/day and 3.9 kWh/day, respectively. The daily averages of the AC electricity usage in the living room and the bedroom are 2.7 kWh and 4.4 kWh, respectively. The fraction of the daily peak demand from all targeted dwellings shows that the peak at the nighttime is due to the use of the air conditioner. Moreover, the patterns of hourly indoor thermal environment showed an average of 30.1 °C (living room) and 29.3 °C (bedroom). Generally, the comfort level for the indoor temperature lies between 23 °C and 26 °C, while for relative humidity, the comfortable range is between 40% and 70%. These findings provide preliminary understanding of the detailed characteristics of residential electricity use and indoor environment in Malaysian dwellings and can be used to predict the electricity demand in the future.

Estimation of domestic urban electricity consumption: A case study of Baghdad, Iraq

Electricity consumption for household purposes in urban areas widely affects the general urban consumption compared to other commercial and industrial uses, as household electricity consumption is affected by many factors related to the physical aspects of the residential area such as temperature, housing unit area, and coverage ratio, as well as social and economic factors such as family size and income, to reach the extent of the influence of each of the above factors on the amount of electricity consumed for residential uses, a selected sample of a residential area in the city of Baghdad was studied and a field survey conducted of the characteristics of that sample and the results analyzed and modeled statistically in relation to the amount of electricity consumed for residential units. Results showed that the annual electricity consumption is directly proportional to both the increase in the residential plot area and the increase in the number of residents of the housing unit and the increase in the monthly income of households, while it is inversely proportional to the plot coverage rate. Through the results of the field research, it was possible to build six quantitative models describing the behavior of electricity consumption in relation to the variables covered in the research.