Forecasting medium-term electricity demand in a South African electric power supply system

Sigauke, Caston

doi:10.17159/2413-3051/2017/v28i4a2428

Cited by 18 publications

(19 citation statements)

References 31 publications

(35 reference statements)

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“…Let y t,τ be hourly GHI and let there be K methods used to predict the next m observations of y t,τ , that is, m is the total number of forecasts. The combined forecasts, z t,τ are then given by Equation 13 [32,33]:…”

Section: Convex Combinationmentioning

confidence: 99%

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

et al. 2019

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Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid.

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Section: Convex Combinationmentioning

confidence: 99%

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

et al. 2019

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“…A comparative analysis will be done with the GAM given in Equation (11) and discussed in Sigauke [31]:…”

Section: Additive Quantile Regression Modelmentioning

confidence: 99%

Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

2018

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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. Except for the AQR model with interactions, the other two models (convex combination model and QRA model) gave prediction interval coverage probabilities that were valid for the 90%, 95% and the 99% prediction intervals. The QRA model had the smallest prediction interval normalised average width and prediction interval normalised average deviation. The modelling framework discussed in this paper has established that going beyond summary performance statistics in forecasting has merit as it gives more insight into the developed forecasting models.

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“…Analyzing the scientific literature, one can remark that when forecasting the electricity consumption, different forecasting time horizons are of interest, encompassing short- [3,14,19,22,24,26,28], medium- [19,21,26], and long-term timeframes [10,13,14,20,[23][24][25]64], each of them bringing their own particular advantages in line with the actual requirements and business needs of the contractors. We targeted the hourly month-ahead electricity prediction, considering the numerous benefits that such a forecast brings to the largeelectricity commercial center-type consumer, ranging from the negotiation and choosing of the most appropriate hourly billing tariffs and correct estimations for the month-ahead electricity consumption submitted to the dispatcher to proper decisional support in what concerns the return on investment in more energy efficient equipment and assessing expanding options.…”

Section: Discussionmentioning

confidence: 99%

“…In the context of the worldwide ever-increasing electricity demand, Mir et al present an insight of the forecasting approaches designed in the scientific literature in order to estimate future electricity demands in the cases of countries having low and middle incomes [19]. The devised study takes into account different prediction methodologies from the body of knowledge, targeting various time horizons, and remark that in the cases when forecasting for long [20] or medium time [21] intervals, the models based on time series have been mainly used, while for short term intervals [22], the techniques have made use of artificial intelligence (AI). The authors remark that in each country the electricity demand is influenced by a series of determinants, such as the population of the respective country, the gross domestic product (GDP), the local weather conditions, and the consumption for different moments and intervals of time (as local consumption habits).…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

Petroşanu

Pîrjan

2020

Sustainability

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The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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Forecasting medium-term electricity demand in a South African electric power supply system

Abstract: The paper discusses an application of generalised additive models (GAMs)

Cited by 18 publications

References 31 publications

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

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