Short-Term and Medium-Term Load Forecasting for Jordan's Power System

Badran, I.; El-Zayyat, Hafez; Halasa, Ghassan

doi:10.3844/ajassp.2008.763.768

Cited by 21 publications

(11 citation statements)

References 1 publication

(1 reference statement)

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“…These methods forecast power demand by using predicted temperature as forecast information. But, when the temperature curves change rapidly on the forecast day, loads change greatly and the forecasting error increases (). Typically, load forecasting can be long term, medium term, short term, or very short term.…”

Section: A Review Of Alternative Modelsmentioning

confidence: 99%

Multivariate dynamic regression: modeling and forecasting for intraday electricity load

Migon

Alves

2013

Appl Stoch Models Bus & Ind

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This paper introduces electricity load curve models for short-term forecasting purposes. A broad class of multivariate dynamic regression models is proposed to model hourly electricity load. Alternative forecasting models, special cases of our general model, include separate time series regressions for each hour and week day. All the models developed include components that represent trends, seasons at different levels (yearly, weekly, etc.), dummies to take into account weekends/holidays and other special days, and short-term dynamics and weather regression effects, discussing the necessity of nonlinx ear functions for cooling effects. Our developments explore the facilities of dynamic linear models such as the use of discount factors, subjective intervention, variance learning and smoothing/filtering. The elicitation of the load curve is considered in the context of subjective intervention analysis, which is especially useful to take into account the adjustments for daylight savings time. The theme of combination of probabilistic forecasting is also briefly addressed.variance learning, is a novelty, as far as we know, in the area of electricity load forecasting. All the models developed include components that represent trends, seasons at different levels (yearly, weekly etc.), dummies to take into account weekends/holidays and other special days, and short-term dynamics and weather regression effects, including nonlinear functions for cooling effects. A comparison between multivariate and univariate models shows that they mainly differ in computational cost. From the point of view of the distribution network operator, this is relevant information, especially because the univariate models takes less time to be fitted.Special care is taken with the role played by weekends/holidays and their effect on the estimated coefficients. In particular, we explore the similarities between the shapes of load curves occurring on the day just before and just after weekends/holidays. Our developments explore the facilities of dynamic linear models such as the use of discount factors, subjective intervention, variance learning and smoothing. The elicitation of the load curve is considered in the context of subjective intervention analysis, which is especially useful to take into account the adjustments for daylight savings time.More precisely, we model a suitable transformation of the electricity load at hour and day t as a linear combination of the transformed load at the same hour on the previous day and appropriate functions of the prevailing temperatures on day t . The coefficients depend on the pair . ; t / in a continuous nonlinear manner. The paper ends by briefly addressing the theme of combination of probabilistic forecasting.The remainder of the paper is organized as follows. A review of alternative models is presented in Section 2. In Section 3, we explore the data set analyzed. Some stylized facts always presented in the load electricity data are also reviewed. Our application to Brazilian southeast hourly electrici...

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Section: A Review Of Alternative Modelsmentioning

confidence: 99%

Multivariate dynamic regression: modeling and forecasting for intraday electricity load

Migon

Alves

2013

Appl Stoch Models Bus & Ind

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“…Also, the authors in El-Telbany and El-Karmi (2008) forecasted the Jordanian electricity demand with an artificial neural network, which was trained by means of particle swarm optimization techniques. This market was also studied in Badran et al (2008) but, this time, the authors preferred to concentrate on short and medium-term load forecasting by using regression models. Finally, Wang and Wang (2008) proposed a new prediction approach based on support vector machines (SVM) techniques with a previous selection of features from data sets by using an evolutionary method.…”

Section: Energy Time Series Forecastingmentioning

confidence: 99%

Discovery of motifs to forecast outlier occurrence in time series

Martínez‐Álvarez

Troncoso

Riquelme

et al. 2011

Pattern Recognition Letters

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a b s t r a c tThe forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurrence of outliers in time series, based on the discovery of motifs. In this sense, motifs will be those pattern sequences preceding certain data marked as anomalous by the proposed metaheuristic in a training set. Once the motifs are discovered, if data to be predicted are preceded by any of them, such data are identified as outliers, and treated separately from the rest of regular data. The forecasting of outlier occurrence has been added as an additional step in an existing time series forecasting algorithm (PSF), which was based on pattern sequence similarities. Robust statistical methods have been used to evaluate the accuracy of the proposed approach regarding the forecasting of both occurrence of outliers and their corresponding values. Finally, the methodology has been tested on six electricity-related time series, in which most of the outliers were properly found and forecasted.

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“…In addition, artificial intelligence has been introduced based on neural network, fuzzy logic, neuro-fuzzy system and genetic algorithm. Forecasting short, medium and long term electric load consumption with artificial neural network has received more attention because of its easy implementation, accuracy and good performance (Abd, 2009;Harun et al, 2009;Senabre et al, 2010;Filik and Kurban, 2007;Liu and Li, 2006;Badran et al, 2008). (James et al, 2005) in their study compare the accuracy and performance of several methods for load forecasting for lead times up to a day-ahead.…”

Section: Introductionmentioning

confidence: 99%

Forecasting and Modelling Electricity Demand Using Anfis Predictor

S.¹

2011

Journal of Mathematics and Statistics

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Problem statement: Load forecasting plays an important task in power system planning, operation and control. It has received an increasing attention over the years by academic researchers and practitioners. Control, security assessment, optimum planning of power production required a precise short term load forecasting. Approach: This study tries to combine neural network and fuzzy logic for next week electric load forecasting. The suitability of the proposed approach is illustrated through an application to electric load consumption data in 2010 downloaded from the RTE France website. Results: The study presents the results and evaluates them. Corresponding code was developed and used to forecast the next week load in a practical power system and the final forecasting result is perfect and consistent. Conclusion: The ANFIS system provides a useful and suitable tool especially for the load forecasting. The forecasting accuracy is high.

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Short-Term and Medium-Term Load Forecasting for Jordan's Power System

Cited by 21 publications

References 1 publication

Multivariate dynamic regression: modeling and forecasting for intraday electricity load

Multivariate dynamic regression: modeling and forecasting for intraday electricity load

Discovery of motifs to forecast outlier occurrence in time series

Forecasting and Modelling Electricity Demand Using Anfis Predictor

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