Modelling and optimisation of long-term forecasting of electricity demand in oil-rich area, South Iran

2021 ◽  
pp. 1-11
Author(s):  
Meysam Pourarshad ◽  
Younes Noorollahi ◽  
Farideh Atabi ◽  
Mostafa Panahi
Keyword(s):  
Electricity Demand ◽  
Long Term Forecasting

scholarly journals Econometric Model for Forecasting Electricity Demand of Industry and Construction Sectors in Vietnam to 2030

2020 ◽  
Vol 3 (1) ◽  
pp. First
Author(s):  
Viết Cường Võ ◽  
Phuong Hoang Nguyen ◽  
Luan Le Duy Nguyen ◽  
Van-Hung Pham
Keyword(s):  
Power System ◽  
Econometric Model ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Master Plan ◽  
Base Scenario ◽  
Leading Position ◽  
Long Term Forecasting ◽  
High Scenario

An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the world, and its electricity demand has been increased dramatically high of about 15%/y for the last three decades. Contribution of industry and construction sectors in GDP has been increasing year by year, and are currently holding the leading position of largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate the electricity consumption of these sectors correctly makes a crucial contribution to the planning of the power system. This paper applies an econometric model with Cobb Douglas production function - a top-down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are the value of the sectors in GDP, income per person, the proportion of electricity consumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for long-term forecasting. Variable of electric price does not affect the demand. This is a very critical result of the study for authority governors in Vietnam. In the base scenario of the GDP and the income per person, the forecasted electricity demands of the sectors are 112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high scenario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above values in the high scenario are less than from 9.0% to 15.8 % of that of the based on in the Revised version of master plan N0. VII.

Development of Social and Economic Forecasting and Ideas of A. Anchishkin

2013 ◽  
pp. 143-155
Author(s):  
A. Klepach ◽  
G. Kuranov
Keyword(s):  
Economic Planning ◽  
Special Role ◽  
Economic Management ◽  
Economic Forecasting ◽  
Russian Economy ◽  
World Economic ◽  
Long Term Forecasting ◽  
Working Out

The role of the prominent Soviet economist, academician A. Anchishkin (1933—1987), whose 80th birth anniversary we celebrate this year, in the development of ideas and formation of economic forecasting in the country at the time when the directive planning acted as a leading tool of economic management is explored in the article. Besides, Anchishkin’s special role is noted in developing a comprehensive program of scientific and technical progress, an information basis for working out long-term forecasts of the country’s development, moreover, his contribution to the creation of long-term forecasting methodology and improvement of the statistical basis for economic analysis and economic planning. The authors show that social and economic forecasting in the period after 1991, which has undertaken a number of functions of economic planning, has largely relied on further development of Anchishkin’s ideas, at the same time responding to new challenges for the Russian economy development during its entry into the world economic system.

Pension Reform in Russia: Assessing the Susceptibility of GDP Production to a Shift in the Number of the Working-Age Population

2019 ◽  
pp. 80-86
Author(s):  
T. P. Skufina ◽  
S. V. Baranov
Keyword(s):  
Pension Reform ◽  
Fixed Capital ◽  
Economic Effects ◽  
Pension Scheme ◽  
Retirement Age ◽  
Working Age ◽  
Working Age Population ◽  
Long Term Forecasting ◽  
The Impact

The presented study considers the susceptibility of gross domestic product (GDP) production to a shift in the number of the working-age population due to an increase in retirement age starting with 2019.Aim. The study aims to examine the quantitative assessments of GDP production in Russia with allowance for the changes in the number of the working-age population due to an increase in the actual retirement age.Tasks. The authors forecast the number of the working-age population with allowance for an increase in the retirement age; develop a model to establish a correlation between the number of the workingage population, investment in fixed capital, and GDP production; quantify the impact of the shift in the number of the working-age population on GDP production in Russia. Methods. This study is based on the results of modeling and long-term forecasting.Results. An economic-mathematical model to establish a correlation between the number of the working-age population, investment in fixed capital, and GDP production is presented. To specify the economic effects of a shift in the number of the working-age population due to an increase in the retirement age, Russia’s GDP production is forecasted for the “old” and “new” (increased retirement age) pension scheme. The forecast is provided for three variants of the number of the working-age population.Conclusions. It is found that with the “old” pension scheme with a lower retirement age GDP production across all three variants will decrease by 2036 compared to 2017. With regard to the “new” scheme that increases the retirement age, it is concluded that an increase in the retirement age is a factor that facilitates GDP production. However, its effect on economic growth will be insignificant.

Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

2021 ◽  
Vol 35 (4) ◽  
pp. 1149-1166
Author(s):  
Hossien Riahi-Madvar ◽  
Majid Dehghani ◽  
Rasoul Memarzadeh ◽  
Bahram Gharabaghi
Keyword(s):  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
River Flows ◽  
Long Term Forecasting

scholarly journals Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting

2021 ◽  
Author(s):  
Kai Chen ◽  
Twan van Laarhoven ◽  
Elena Marchiori
Keyword(s):  
Gaussian Processes ◽  
Multivariate Time Series ◽  
Heavy Tail ◽  
Lottery Ticket ◽  
Spectral Densities ◽  
Heavy Tailed ◽  
Long Term Forecasting ◽  
The Time Domain ◽  
Spectral Mixture

AbstractLong-term forecasting involves predicting a horizon that is far ahead of the last observation. It is a problem of high practical relevance, for instance for companies in order to decide upon expensive long-term investments. Despite the recent progress and success of Gaussian processes (GPs) based on spectral mixture kernels, long-term forecasting remains a challenging problem for these kernels because they decay exponentially at large horizons. This is mainly due to their use of a mixture of Gaussians to model spectral densities. Characteristics of the signal important for long-term forecasting can be unravelled by investigating the distribution of the Fourier coefficients of (the training part of) the signal, which is non-smooth, heavy-tailed, sparse, and skewed. The heavy tail and skewness characteristics of such distributions in the spectral domain allow to capture long-range covariance of the signal in the time domain. Motivated by these observations, we propose to model spectral densities using a skewed Laplace spectral mixture (SLSM) due to the skewness of its peaks, sparsity, non-smoothness, and heavy tail characteristics. By applying the inverse Fourier Transform to this spectral density we obtain a new GP kernel for long-term forecasting. In addition, we adapt the lottery ticket method, originally developed to prune weights of a neural network, to GPs in order to automatically select the number of kernel components. Results of extensive experiments, including a multivariate time series, show the beneficial effect of the proposed SLSM kernel for long-term extrapolation and robustness to the choice of the number of mixture components.

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