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The increasing penetration of renewable energy sources tends to redirect the power systems community's interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Exploratory data analysis (EDA) is often a necessary task in uncovering hidden patterns, detecting outliers, and identifying important variables and any anomalies in data. Furthermore, the approach can be used to gain insights by modelling the dataset through graphical representations. In this paper, we propose an exploratory framework for analysing a road traffic accidents real-life dataset using graphical representations and incorporating dimensionality reduction methods. Both Principal component and Linear discriminant analyses are performed on the dataset and the resulting performance metrics reveal some comprehensive insights of the road traffic accident patterns. The investigation also revealed which road traffic factors contribute more significantly to the events. Classification results were generated after applying the dimensionality reduction methods to the dataset and show that the application of Linear discriminant analysis dimensionality reduction together with Naïve Bayes classification performed better as compared to the other approaches for the dataset. [ABSTRACT FROM AUTHOR]