Economic Dispatch of Electrical Power in South Africa: An Application to the Northern Cape Province (original) (raw)
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Applied Energy, 2018
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Density forecasting for long-term electricity demand in South Africa using quantile regression
South African Journal of Economic and Management Sciences
Background: This study involves forecasting electricity demand for long-term planning purposes. Long-term forecasts for hourly electricity demands from 2006 to 2023 are done with in-sample forecasts from 2006 to 2012 and out-of-sample forecasts from 2013 to 2023. Quantile regression (QR) is used to forecast hourly electricity demand at various percentiles. Three contributions of this study are (1) that QR is used to generate long-term forecasts of the full distribution per hour of electricity demand in South Africa; (2) variabilities in the forecasts are evaluated and uncertainties around the forecasts can be assessed as the full demand distribution is forecasted and (3) probabilities of exceedance can be calculated, such as the probability of future peak demand exceeding certain levels of demand. A case study, in which forecasted electricity demands over the long-term horizon were developed using South African electricity demand data, is discussed. Aim: The aim of the study was: (1...
Energies, 2021
It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insig...
Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models
Energies, 2018
Short-term hourly load forecasting in South Africa using additive quantile regression (AQR) models is discussed in this study. The modelling approach allows for easy interpretability and accounting for residual autocorrelation in the joint modelling of hourly electricity data. A comparative analysis is done using generalised additive models (GAMs). In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso) via hierarchical interactions. Four models considered are GAMs and AQR models with and without interactions, respectively. The AQR model with pairwise interactions was found to be the best fitting model. The forecasts from the four models were then combined using an algorithm based on the pinball loss (convex combination model) and also using quantile regression averaging (QRA). The AQR model with interactions was then compared with the convex combination and QRA models and the QRA model gave the most accurate forecasts. ...
2017
Electricity peak demand forecasting is a key exercise undertaken to avoid power blackouts and system failure. In this paper, the next day’s peak load demand is estimated and forecasted. The challenge is to generate a peak demand forecast that avoids the risk of a power blackout. We approximate the upper bound for the electricity demand utilizing estimated quantiles by quantile regression and triangular distribution. The upper bounds constructed are compared with the actual electricity demand. The proposed method successfully constructs the upper bound to avoid underprediction; i.e., it avoids the risk of power blackouts.
International Journal of Energy Economics and Policy, 2018
Peak load demand forecasting is a key exercise undertaken to avoid system failure and power blackouts. In this paper, the next day’s peak load demand is forecasted. The challenge is to estimate a model that is capable of preventing underprediction of the peak load demand: in other words, a model that is competent in forecasting the upper bound of the peak demand to avoid the risk of power blackouts. First, quantile regression is performed to generate forecasts of the daily peak load demand. Then, peak demand forecasts are locally approximated by triangular distribution to generate the upper bound of the peak demand. The forecasted upper bounds are compared with the actual electricity demand. The proposed method succeeds in avoiding underprediction of the peak load demand and thus the risk of power blackouts.
Journal of Statistics and Management Systems, 2016
In a developing country such as South Africa, understanding the expected future demand for electricity is very important in various planning contexts. It is specifically important to understand how expected scenarios regarding population or economic growth can be translated into corresponding future electricity usage patterns. This paper discusses a methodology for forecasting long-term electricity demand that was specifically developed for applying to such scenarios. The methodology uses a series of multiple regression models to quantify historical patterns of electricity usage per sector in relation to patterns observed in certain economic and demographic variables, and uses these relationships to derive expected future electricity usage patterns. The methodology has been used successfully to derive forecasts used for strategic planning within a private company as well as to provide forecasts to aid planning in the public sector. This paper discusses the development of the modelling methodology, provides details regarding the extensive data collection and validation processes followed during the model development, and reports on the relevant model fit statistics. The paper also shows that the forecasting methodology has to some extent been able to match the actual patterns, and therefore concludes that the methodology can be used to support planning by translating changes relating to economic and demographic growth, for a range of scenarios, into a corresponding electricity demand. The methodology therefore fills a particular gap within the South African long-term electricity forecasting domain.
Short-term peak electricity demand in South Africa
AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 2012
The paper discusses the modelling and forecasting of daily winter peak electricity loads in South Africa for the period 2000 to 2009 using a regression model that allows for nonlinear and nonparametric terms.A demand model for daily winter peaks is developed and used for out of sample predictions. Empirical results show that electricity demand in South Africa is highly sensitive to temperature fluctuations during the winter periods.
Regression-SARIMA modelling of daily peak electricity demand in South Africa
Journal of Energy in Southern Africa, 2017
In this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them with Winter’s triple exponential smoothing model. Empirical results from the study show that the SARIMA model produces more accurate short-term forecasts. The regression-SARIMA modelling framework captures important drivers of electricity demand. These results are important to decision makers, load forecasters and systems operators in load flow analysis and scheduling of electricity.