Speed forecasting has numerous applications in intelligent transport systems' design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles' speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles' characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.
In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.
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