@conference {Sarhani20151, title = {Electric load forecasting using hybrid machine learning approach incorporating feature selection}, booktitle = {CEUR Workshop Proceedings}, volume = {1580}, year = {2015}, note = {cited By 0}, pages = {1-7}, abstract = {Forecasting of future electricity demand is very important for the electric power industry. As influenced by various factors, it has been shown in several publications that machine learning methods are useful for electric load forecasting (ELF). On the one hand, we introduce in this paper the approach of support vector regression (SVR) for ELF. In particular, we use particle swarm optimization (PSO) algorithm to optimize SVR parameters. On the other hand, it is important to determine the irrelevant factors as a preprocessing step for ELF. Our contribution consists of investigating the importance of applying the feature selection approach for removing the irrelevant factors of electric load. The experimental results elucidate the feasibility of applying feature selection without decreasing the performance of the SVR-PSO model for ELF.}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977574358\&partnerID=40\&md5=405be8fb16c29c43f82f16549d26252b}, author = {Sarhani, M. and El Afia, A.} } @conference {Sarhani201479, title = {An extension of X13-ARIMA-SEATS to forecast islamic holidays effect on logistic activities}, booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014}, year = {2014}, note = {cited By 0}, pages = {79-84}, abstract = {To better manage and optimize logistic activities, factors that affect it must be determined: The calendar effect is one of these factors which must be analyzed. Analyzing such kind of data by using classical time series forecasting methods, such as exponential smoothing method and ARIMA model, will fail to capture such variation. This paper is released to present a review of the models which are used to forecast the calendar effect, especially moving holidays effect. We adopt the recent approach of X13-ARIMA-SEATS and extend it for being able to forecast the effect of Islamic holidays. Our extension is applied to Moroccan case studies, and aims to give recommendations concerning this effect on logistic activities. {\textcopyright} 2014 IEEE.}, doi = {10.1109/GOL.2014.6887423}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908655728\&doi=10.1109\%2fGOL.2014.6887423\&partnerID=40\&md5=de88391f3f7ec0517e753c7aeb4aeae4}, author = {Sarhani, M. and El Afia, A.} }