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Evaluating the Effectiveness of Parametric and Nonparametric Energy Consumption Forecasts for a Developing Country

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Abstract

This paper seeks to analyse and evaluate Sri Lanka's energy consumption forecasts, more specifically, electricity, petroleum, coal and renewable electricity consumption using a variety of parametric and nonparametric forecasting techniques. The Sri Lankan economy is emerging following the end of a prolonged civil war, and thus this topic is opportune as accurate forecasts of energy requirements are indispensable for sustaining the ongoing rapid economic expansion. We also consider evaluating the appropriateness of parametric and nonparametric energy forecasting methods for Sri Lanka. In addition, this paper marks the introductory application of SSA for forecasting in Sri Lanka, and the results from SSA are compared against the popular benchmarks of ARIMA, ETS, HW, TBATS, and NN. We find statistically significant evidence proving that the SSA model outperforms ETS and HW at forecasting renewable electricity consumption, and also, that the SSA model outperforms ARIMA and TBATS models at forecasting coal consumption. On average, the SSA model is found to be best for energy consumption forecasting in Sri Lanka whilst the Neural Networks model is second best.

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... In line with this, there were two methods based on frequency domain, which were Fourier Series Analysis (FSA) and Singular Spectrum Analysis (SSA). Both of them did not have to fulfill the parametric assumption, so that they were categorized in nonparametric methods[3][4]. Technically, SSA could divide data based on its elements such as trend, seasonal, cyclical, and noise. ...
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... In brief, the TBATS technique uses a new method that greatly reduces the computational burden in the maximum likelihood estimation when forecasting complex seasonal time series such as those with multiple seasonal peri- ods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects (De Livera et al., 2011). TBATS has been used to forecast energy consumption (Silva & Rajapaksa, 2014), the price of gold (Hassani, Silva, Gupta, & Segnon, 2015) and housing downturns (Zietz & Traian, 2014) in previous studies. Thirdly, all the aforementioned techniques are classical meth- ods, and SSA is able to provide a completely different modelling approach as SSA is a filtering technique. ...
... The possible applications areas of Singular Spectrum Analysis are diverse: from mathematics and physics to economic and financial mathematics, from social science and market research to meteorology and oceanology (e.g. Hassani, 2007;Hassani, Mahmoudvand, & Zokaei, 2011;Hassani, Soofi, & Zhigljavsky, 2013a;Hassani & Zhigljavsky, 2008;Sanei, Ghodsi, & Hassani, 2010;Silva & Rajapaksa, 2014 and references therein). The aim of Singular Spectrum Analysis is to make a decomposition of the original series into the sum of a small number of independent and interpretable componentssuch as a slowly varying trend, oscillatory components and a structureless noise (Hassani, 2007). ...
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An artificial neural network model is developed to relate the electric energy consumption in the Eastern Province of Saudi Arabia to the weather data (temperature and humidity), global solar radiation and population. A two layered feedforward neural network is used for the modelling. The inputs to the neural network are the independent variables and the output is the electric energy consumption. Seven years' of data are used for model building and validation. Model adequacy is established by a visual inspection technique and the chi-square test. Model validation, which reflects the suitability of the model for future predictions is performed by comparing the predictions of the model with future data that was not used for model building. Comparison with a regression model shows that the neural network model performs better for predictions.
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In the early 2000s, the Republic of Turkey has initiated an ambitious reform program in her electricity market, which requires privatization, liberalization as well as a radical restructuring. The most controversial reason behind, or justification for, recent reforms has been the rapid electricity demand growth; that is to say, the whole reform process has been a part of the endeavors to avoid the so-called “energy crisis”. Using cointegration analysis and autoregressive integrated moving average (ARIMA) modelling, the present article focuses on this issue by both providing an electricity demand estimation and forecast, and comparing the results with official projections. The study concludes, first, that consumers’ respond to price and income changes is quite limited and therefore there is a need for economic regulation in Turkish electricity market; and second, that the current official electricity demand projections highly overestimate the electricity demand, which may endanger the development of both a coherent energy policy in general and a healthy electricity market in particular.
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The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model.The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population.A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to −0.06, while long run elasticities are equal to −0.24 and −0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values.In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models.A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account.
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We address a problem faced by every supplier of electricity, i.e. forecasting the short-term electricity consumption. The introduction of new techniques has often been justified by invoking the nonlinearity of the problem. Our focus is directed to the question of deciding whether the problem is indeed nonlinear. First, we introduce a nonlinear measure of statistical dependence. Second, we analyse the linear and the nonlinear autocorrelation functions of the Czech electric consumption. Third, we compare the predictions of nonlinear models (artificial neural networks) with linear models (of the ARMA type). The correlational analysis suggests that forecasting the short-term evolution of the Czech electric load is primarily a linear problem. This is confirmed by the comparison of the predictions. In the light of this case study, the conditions under which neural networks could be superior to linear models are discussed.
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Given two sources of forecasts of the same quantity, it is possible to compare prediction records. In particular, it can be useful to test the hypothesis of equal accuracy in forecast performance. We analyse the behaviour of two possible tests, and of modifications of these tests designed to circumvent shortcomings in the original formulations. As a result of this analysis, a recommendation for one particular testing approach is made for practical applications.
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We propose a test of the null hypothesis that an observable series is stationary around a deterministic trend. The series is expressed as the sum of deterministic trend, random walk, and stationary error, and the test is the LM test of the hypothesis that the random walk has zero variance. The asymptotic distribution of the statistic is derived under the null and under the alternative that the series is difference-stationary. Finite sample size and power are considered in a Monte Carlo experiment. The test is applied to the Nelson-Plosser data, and for many of these series the hypothesis of trend stationarity cannot be rejected.
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Because South Korea's industries depend heavily on imported energy sources (fifth largest importer of oil and second largest importer of liquefied natural gas in the world), the accurate estimating of its energy demand is critical in energy policy-making. This research proposes an artificial neural network model (a structure with feed-forward multilayer perceptron, error back-propagation algorithm, momentum process, and scaled data) to efficiently estimate the energy demand for South Korea. The model has four independent variables, such as gross domestic product (GDP), population, import, and export amounts. The data are obtained from diverse local and international sources. The proposed model better estimated energy demand than a linear regression model (a structure with multiple linear variables and least square method) or an exponential model (a structure with mixed integer variables, branch and bound method, and Broyden–Fletcher–Goldfarb–Shanno (BFGS) method) in terms of root mean squared error (RMSE). The model also forecasted better than the other two models in terms of RMSE without any over-fitting problem. Further testing with four scenarios based upon reliable source data showed unanticipated results. Instead of growing permanently, the energy demands peaked at certain points, and then decreased gradually. This trend is quite different from the results by regression or exponential model.
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Energy dependency (ED) implies the extent to which an economy relies upon imports in order to meet its energy needs. The ED is calculated as net imports divided by the sum of gross inland energy consumption plus bunkers. This study aims at obtaining numerical equations to estimate of Turkey's energy dependency based on basic energy indicators and sectoral energy consumption by using artificial neural network (ANN) technique. It seeks to contribute to the strategies necessary to preserve the supply–demand balance of Turkey. For this purpose, two different models were used to train the ANN approach. In Model 1, main energy indicators such as total production of primary energy per capita, total gross electricity generation per capita and final energy consumption per capita were used in the input layer of the ANN while sectoral energy consumption per capita was used in Model 2.The ED was in the output layer for both models. Different models were employed to estimate the ED with a high confidence for future projections. The R2 values of ED were found to be 0.999 for both models. In accordance with the analysis results, ED is expected to increase from 72% to 82% within 14 years of period. Consequently, the utilization of renewable energy sources and nuclear energy is strictly recommended to ensure the ED stability in Turkey.
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In this study, we used ARIMA, seasonal ARIMA (SARIMA) and alternatively the regression model with seasonal latent variable in forecasting electricity demand by using data that belongs to "Kayseri and Vicinity Electricity Joint-Stock Company" over the 1997:1-2005:12 periods. This study tries to examine the advantages of forecasting with ARIMA, SARIMA methods and with the model has seasonal latent variable to each other. The results support that ARIMA and SARIMA models are unsuccessful in forecasting electricity demand. The regression model with seasonal latent variable used in this study gives more successful results than ARIMA and SARIMA models because also this model can consider seasonal fluctuations and structural breaks.
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Online short-term load forecasting is needed for the real-time scheduling of electricity generation. Univariate methods have been developed that model the intraweek and intraday seasonal cycles in intraday load data. Three such methods, shown to be competitive in recent empirical studies, are double seasonal ARMA, an adaptation of Holt-Winters exponential smoothing for double seasonality, and another, recently proposed, exponential smoothing method. In multiple years of load data, in addition to intraday and intraweek cycles, an intrayear seasonal cycle is also apparent. We extend the three double seasonal methods in order to accommodate the intrayear seasonal cycle. Using six years of British and French data, we show that for prediction up to a day-ahead the triple seasonal methods outperform the double seasonal methods, and also a univariate neural network approach. Further improvement in accuracy is produced by using a combination of the forecasts from two of the triple seasonal methods.
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In 2002 the Government of Sri Lanka proposed power sector policy guidelines for the first time in its history in order to facilitate the restructuring of the sector. This paper attempts to critically examine and appraise the Government’s proposals with suggestions for improvements. The methodology employed is to first examine the requirements of the Sri Lankan power sector by analysing the current problems that the power sector faces and to empirically estimate electricity demand to identify the future consumption and capacity expansion needs of the sector. Secondly, it is assessed to what extent the proposed reforms address the requirements of the sector identified above. Finally, alternative proposals are introduced in order to address the identified flaws in the current proposed reforms.
Article
This paper, has tried to forecast monthly maximum electricity demand for the state Maharashtra, India, using Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) method for seasonally unadjusted monthly data spanning from April 1980 to June 1999. The forecasted period is 18 months ahead from June 1999. This study's basic findings are that the series does not reveal any drastic change for the forecasted period. It continues to follow the same trend along with the seasonal variation. Copyright 2002 by Taylor and Francis Group