Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches

Nasr, G. E.; Badr, E. A.; Younes, M. R.

doi:10.1002/er.766.abs

Cited by 7 publications

(9 citation statements)

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“…Because it is a well-known universal estimator, the ANN model can rather be considered as standard benchmark (Gencoglu and Uyar, 2009;Nasr et al, 2002). ANN architecture used is illustrated in Fig.…”

Section: Network Architecture and Optimum Elm And Ann Modelmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

261

102

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The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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Section: Network Architecture and Optimum Elm And Ann Modelmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

261

102

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“…In addition, to show the potential of the proposed model, a performance comparison in terms of the estimation capability and the learning speed was made between the proposed model and conventional feedforward ANN model with BP. Because it is a well-known universal estimator, the results from ANN can be considered as a rather standard benchmark [22][23][24]. ANN architecture used in this study was illustrated in Figure 5.…”

Section: Resultsmentioning

confidence: 99%

Application of extreme learning machine for estimating solar radiation from satellite data

Şahin

Kaya

Uyar

et al. 2013

Int. J. Energy Res.

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SUMMARYIn this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell.

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“…Charbaji (2001) developed a multivariate statistical model to classify commercial banks in Lebanon into cohesive categories based on their financial ratios. Nasr et al (2002) presented an artificial neural network approach to electric energy consumption forecasting in Lebanon. Sbayti et al (2002) investigated the effect of roadway network aggregation levels on modeling of traffic-induced emission inventories in Beirut.…”

Section: Lebanonmentioning

confidence: 99%

Operations research/management science in the Arab world: historical development

Yousef

2010

International Transactions in Operational Research

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This study aims to review the related literature on operations research (OR)/management science (MS) in the Arab world published during the last three decades. Owing to the nature of this study, an extensive survey of the related literature was conducted and inferences were drawn. The inferences drawn from the literature survey on OR/MS in the Arab world were first, there is a paucity of published real‐world applications of OR/MS. Second, there is a lack of published survey‐type studies in most Arab countries, except in Jordan, Lebanon, Palestine, Syria, and the United Arab Emirates. Third, the majority of published works on OR/MS were of a conceptual nature. A few papers concerned with OR/MS in the Arab world were published during the 1980s, with a special emphasis on conceptual issues rather than on applied or survey‐type studies. The 1990s witnessed an increased number of publications on both survey‐type and conceptual studies. Since 2000, the number of publications has increased substantially, mainly through conceptual studies. This study has a number of implications for both practitioners and researchers. Practitioners will be made aware of the applications of OR/MS in the Arab world and the type of problems that have been addressed. This, in turn, might motivate the decision makers and the managers to adopt OR/MS approaches in solving their organizations' problems. As a result, this might increase the usage of OR/MS in this part of the world. Researchers will be able to identify the OR/MS research areas that need more attention in the Arab world. The study mainly covers the studies that are written in English and indexed in non‐Arabic databases. Although the Arabic works were not surveyed exhaustively, the author reviewed and included some available OR/MS works written in Arabic. This study is considered as the first work of its type in surveying the scholarly publications pertaining to OR/MS in the Arab world since the 1980s.

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Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches

Cited by 7 publications

References 0 publications

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Application of extreme learning machine for estimating solar radiation from satellite data

Operations research/management science in the Arab world: historical development

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