Forecasting urbanization and electricity consumption dynamics in Somalia using ARIMA modeling

The paper examines the relationship between the dynamics of electricity consumption and Nepal's economic prosperity. Descriptive methodologies and statistical tools are used in this quantitative study that gathers data from secondary sources. The patterns of power consumption are influenced by population increase, urbanization, government regulations, and technical developments, all of which affect Nepal's economic development and social well-being. This paper uses an interdisciplinary approach to show how crucial it is to address energy-related issues to realize socioeconomic potential. Economic growth is the main factor driving the dynamic of power consumption. The finding reveals that the autonomous electricity consumption is 4.13 kilowatt hours shows if income increases by USD 100, the estimated increase in average electricity consumption is 54 kWh. Indicates that increase in per capita income, increases the consumption of electricity, and economic growth are strongly correlated. The result from the forecast analysis shows that the unrestrained demand for electricity is projected to increase from an estimated 7,888 million kilowatt-hours (million kWh) in 2019–2020 to 13,831 million kilowatt-hours in 2029–2030 which seems closer and validates the Electricity Demand Forecast for 2014 to 2040 by Government of Nepal. The result shows that the dynamics of electricity consumption and economic prosperity are strongly correlated. Policymakers, energy planners, investors, and other stakeholders must acknowledge the issue and make a call to action to support initiatives that enhance economic growth, access to energy, and sustainable energy use.

The Covid-19 pandemic has caused severe economic depression and has disrupted the supply chains of various industries. The automobile industry which contributes significantly to the Indian economy was gravely hit due to the lockdowns, semiconductor shortage and the uncertainty associated with the pandemic. This research paper analyses the effect of Covid-19 on the automobile sales in India using the time series modelling approach. The data recorded by SIAM from 2012 to 2019 was used to develop the Autoregressive Integrated Moving Average (ARIMA) model following the Box-Jenkins methodology. ARIMA model (2, 1, 3) was chosen as it had the lowest AIC and BIC criteria. This model was used to forecast the sales from 2020 to 2021 to give a picture of the expected automobile sales had the pandemic not occurred. The forecasted data from the model developed has then been compared with actual automobile sales data during the pandemic to gauge the level of impact Covid-19 had on the Indian automobile industry. The paper also explores the associated challenges that the automobile industry had to face due to the pandemic.

This study employed the Box-Jenkins Methodology otherwise known as the Autoregressive Integrated Moving Average (ARIMA) modelling to model and forecast the series for the period of 2018 to 2030. The results indicated an upward trend with fluctuations while the series was stationary at first difference, i.e the series was I(1) . Based on the Akaike information criterion (AIC) and Bayesian Information Criteria (BIC) choice criteria, it was found that ARIMA (2, 1, 2) model was better suited to the import value index (IVI) series. Diagnosed check of the model reveals that the error was random, normally distributed and there was no serial correlation, in the same vein, thirteen years forecast was made which shows fluctuation pattern in import value index (IVI) series.

This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.

Background: The growing health risks associated with greenhouse gas emissions highlight the need for new energy policies that emphasize efficiency and low-carbon energy intensity.Objectives: We assessed the relationships among electricity use, coal consumption, and health outcomes.Methods: Using time-series data sets from 41 countries with varying development trajectories between 1965 and 2005, we developed an autoregressive model of life expectancy (LE) and infant mortality (IM) based on electricity consumption, coal consumption, and previous year’s LE or IM. Prediction of health impacts from the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) integrated air pollution emissions health impact model for coal-fired power plants was compared with the time-series model results.Results: The time-series model predicted that increased electricity consumption was associated with reduced IM for countries that started with relatively high IM (> 100/1,000 live births) and low LE (< 57 years) in 1965, whereas LE was not significantly associated with electricity consumption regardless of IM and LE in 1965. Increasing coal consumption was associated with increased IM and reduced LE after accounting for electricity consumption. These results are consistent with results based on the GAINS model and previously published estimates of disease burdens attributable to energy-related environmental factors, including indoor and outdoor air pollution and water and sanitation.Conclusions: Increased electricity consumption in countries with IM < 100/1,000 live births does not lead to greater health benefits, whereas coal consumption has significant detrimental health impacts.

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.

This paper investigates the application of the Autoregressive Integrated Moving Average (ARIMA) model to predict future trends in Chinese housing prices. The Chinese real estate market, characterized by its volatility, especially during the post-COVID-19 period, presents a complex environment for buyers and investors. The paper investigates how the ARIMA model is employed to make informed predictions in this uncertain market. Although it has some limitations, such as a heavy reliance on historical data and insensitivity to unexpected macroeconomic shifts, the ARIMA model offers a structure for understanding and anticipating housing price trends. The paper integrates various data sources, including long-term housing price statistics, to build a comprehensive ARIMA model tailored to the nuances of China’s housing market. This essay demonstrates the ARIMA model's utility in aiding stakeholders to make more confident and informed decisions in an increasingly complex and unpredictable market. The analysis further suggests refinements to the ARIMA model, considering the multifaceted nature of the housing market, influenced by macroeconomic factors and public perception. The final goal is to enhance the model's accuracy and reliability, making it an indispensable tool in economic and policy decision-making in China's evolving real estate landscape.

Although energy exists in different forms in nature, the most important form of energy is Electricity. Considering the worldwide increasing demand for electricity with increasing population, rapid urbanization, rising incomes, changes in lifestyles and with infrastructural and industrial growth, forecasting electricity consumption becomes an important part of power sector of any country. In this paper, attempt is made to forecast the total electricity consumption in Assam with the help of two models, considering the time period from 1990-91 to 2016-17. The first one is Multiple Linear Regression model which considers the effects of Population and Per Capita Income on the electricity consumption in Assam while the second one is the Autoregressive Integrated Moving Average (ARIMA) model. By comparing the Average Relative Error of the two proposed models, the predictive performance of the ARIMA (0, 1, 2) model is found better than the Multiple Linear Regression model and ARIMA shows an annual average increasing rate of electricity consumption as 5.40% up to 2021.

The objective of this study is to estimate the non-linear effect of energy consumption i.e. oil, gas, electricity, and coal consumption on CO2 emission in South Asian countries. The study uses annual panel data of three South Asian countries i.e. Bangladesh, India, and Pakistan from 1985 to 2017 and applies panel non-linear ARDL methodology to examine the long-run and short-run relationship. Results show that an increase in gas, electricity, coal, and electricity consumption leads to an increase in the carbon dioxide emission, whereas decrease in electricity and coal consumption reduces the carbon dioxide emissions in the long run. Non-linear relationship exists between electricity consumption and CO2 emissions as well as between coal consumption and CO2 emissions in South Asian countries in the long run. Results of short run dynamics of individual countries show that non-linear relationship exists between oil consumption and CO2 emissions, electricity consumption and CO2 emissions, and coal consumption and CO2 emissions in Bangladesh and Pakistan. Research and development centers are required to control pollution through new technologies, while discourage to use higher electricity and coal consumption as a source of energy for a healthier environment.

The dynamics of electricity consumption and economic growth: A revisit study of their causality in Pakistan

For this study, we conducted a decomposition analysis of industrial electricity consumption based on the logarithmic mean Divisia index approach. An empirical dataset consisting of 11 industrial sectors in Korea from 2000 to 2018 was used. The three-factor decomposition equation was extended to include four factors by decomposing the energy intensity effect into electrification and electricity consumption efficiency effects. The empirical results are summarized as follows: The increase in electricity consumption in the Korean industrial sector from 2000 to 2018 is mostly caused by the production effect. While the structure effect decreases electricity consumption, the intensity effect increases it. The key findings indicate that the hidden electrification effect can be confusing to researchers with regard to the intensity effect. The empirical evidence suggests that the intensity effect has a positive effect on electricity consumption induced by the electrification effect, although the efficiency effect continuously decreased electricity consumption. The decomposition results of some sectors show that electrification, rather than the production effect, contributed the most to the increase in electricity consumption. This implies that while replacing fuel with electricity has been successfully achieved in several sectors, there are still challenges regarding increasing energy efficiency and expanding clean electricity generation.

Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.

The present study was carried out to estimate the trends of area, production and productivity of wheat in North Gujarat. Yearly time series data on area, production and productivity of wheat of North Gujarat for the period of 1991-92 to 2021-22 were collected from the published reports by Directorate of Agriculture, Gujarat state, Gandhinagar. Polynomial and Autoregressive Integrated Moving Average (ARIMA) models were applied to analyze the area, production and productivity of wheat in North Gujarat. First, second and third-degree polynomial models were fitted to the original data and moving averages, with the best model selected based on adjusted R², Root Mean Square Error (RMSE), Mean Absolute Error (MAE), normality by the Shapiro-Wilk (1965) test and randomness of residuals by the Run test. ARIMA models were evaluated for stationarity and different orders (p, d, q) were assessed using autocorrelation function (ACF), partial autocorrelation function (PACF) and the models were selected on the basis of significant autoregressive and moving average term, lower value of Akaike’s Information Criteria (AIC) and Schwartz-Bayesian Criteria (SBC) and normality of residuals by Shapiro-Wilk (1965) test and Box-Ljung (1978) test. The cubic polynomial model on a four year moving average was most suitable for wheat production trends, while ARIMA (0, 2, 1) and ARIMA (0, 2, 2) were optimal for wheat area and production patterns.

Purpose This study aims to investigate how disclosing COVID-19 patient visit locations affects electricity consumption in Gwangju, South Korea. It explores whether increased health risk perception leads to risk-averting behavior, such as restricting outdoor activities and increasing indoor energy use. Design/methodology/approach This study employs a spatial difference-in-differences (DID) approach to investigate how disclosing COVID-19 patient visit locations affects electricity consumption in Gwangju, South Korea. The data includes monthly electricity consumption from 261 condominium complexes from January 2019 to December 2020. Complexes within 500 m of disclosed locations are considered the treatment group. Robustness checks involve propensity score matching and staggered DID models. Findings This study finds that disclosing COVID-19 patient visit locations in Gwangju, South Korea, leads to a 6.4% increase in electricity consumption among nearby households. This increase is attributed to risk-averting behavior, where households restrict outdoor activities and spend more time indoors. The study also reveals that social connectedness mitigates this effect; households in less densely populated areas show lower increases in electricity consumption. Robustness checks, including propensity score matching and staggered difference-in-differences models, support these findings. The results highlight how health risk perception and social factors influence energy consumption during pandemics. Originality/value This research offers original insights into how health risk perception affects electricity consumption during the COVID-19 pandemic. This study demonstrates that disclosing COVID-19 patient visit locations leads to increased electricity consumption due to risk-averting behavior. Additionally, this paper documents that social connectedness mitigates this effect, suggesting that community cohesion can reduce the impact of health risks on energy use.

This paper proposes a simple two-step estimation method (Climate Adaptive Response Estimation - CARE) to estimate sectoral climate damage functions, which account for long-run adaptation. The paper applies this method in the context of residential electricity and natural gas demand for the world’s fifth largest economy — California. The advantage of the proposed method is that it only requires detailed information on intensive margin behavior, yet does not require explicit knowledge of the extensive margin response (e.g., technology adoption). Using almost two billion energy bills, we estimate spatially highly disaggregated intensive margin temperature response functions using daily variation in weather. In a second step, we explain variation in the slopes of the dose response functions across space as a function of summer climate. Using 18 climate models, we simulate future demand by letting households vary consumption along the intensive and extensive margins. We show that failing to account for extensive margin adjustment in electricity demand leads to a significant underestimate of the future impacts on electricity consumption. We further show that reductions in natural gas demand more than offset any climate-driven increases in electricity consumption in this context.