Abstract

Electric vehicles (EVs) presence in the power grid can bring about pivotal concerns regarding their energy requirements. EVs charging behaviors can be affected by several aspects including socio-economics, psychological, seasonal among others. This work proposes a case study to analyze seasonal effects on charging patterns, using a public real-world based dataset that contains information from the aggregated load of the total charging stations of Boulder, Colorado. Our approach targets to forecast and recognize EVs demand considering seasonal factors. Principal component analysis (PCA) was used to provide a visual representation of the variables and their contribution and the correlation among them. Then, twelve classification models were trained and tested to discriminate among seasons the charging load of electric vehicles. Later, a benchmark stage is presented for regression as well as for classification results. For regression models, examined through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), the random Forest provides better prediction than quasi-Poisson model widely. However, it was observed that for large variations in electric vehicles’ charging load, quasi-Poisson fits better than random forest. For the classification models, evaluated through Accuracy and the Area under the Curve, the Lasso and elastic-net regularized generalized linear (GLMNET) model provided the best global performance with accuracy up to 100% when evaluated on the test dataset. The results of this work offer great insights for enhancing demand response strategies that involve PEV charging regarding charging habits across seasons. Document type: Article

Abstract

[EN] This article creates and empirically analyzes a dynamic pricing framework. The model includes the main theories on prices in the literature, and dynamically analyzes how companies change their pricing policies according to different circumstances. Through the use of regressions, the article shows the relative long and short-term impact on pricing equations (strategic versus operational matters), and on production conditions versus demand perspectives in the setting of operational prices. In addition, it highlights how the proportions of these pricing perspectives change in the airline sector according to the effect of the environment on the configuration of the strategy (determined by monthly and hourly seasonality), and also according to the nature of this competitive strategy (determined by the characteristics of the company and the destination airport). The results confirm our framework, and show how firms combine the different perspectives in their pricing strategies. We acknowledge financial support from the Valencian Economic Research Institute (IVIE, Valencian Government), and the Universitat Politècnica de València. Narangajavana, Y.; Garrigós Simón, FJ.; Sánchez García, J.; Forgas-Coll, S. (2014). Prices, prices and prices: A study in the airline sector. Tourism Management. 41:28-42. doi:10.1016/j.tourman.2013.08.008 S 28 42 41