Dataset for Multivariate Electricity Consumption Prediction with Extreme Learning Machine

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This repository contains resources used and described in the paper. The repository is structured as follows: NOTE: this should follow the folders you have in this repository algorithms/: Formal description of algorithm for entity normalization and sentence clustering. data/: Dataset used for this paper. code/: Evaluation script. evaluation/: Evaluation script. presentation/: PDF of paper presentation in certain conference or venue. Paper Abstract In this paper, Extreme Learning Machine (ELM) is demonstrated to be a powerful tool for electricity consumption prediction based on its competitive prediction accuracy and superior computational speed compared to Support Vector Machine (SVM). Moreover, ELM is utilized to investigate the potentials of using auxiliary information such as electricity-related factors and environmental factors to augment the prediction accuracy obtained by purely using the electricity consumption factors. Furthermore, we formulate a combinatorial optimization problem of seeking an optimal subset of auxiliary factors and their corresponding optimal window sizes using the most suitable ELM structure, and propose a Discrete Dynamic Multi-Swarm Particle Swarm Optimization (DDMS-PSO) to address this problem. Experimental studies on a real-world building dataset demonstrate that electricity-related factors improve accuracy while environmental factors further boost accuracy. By using DDMSPSO, we find a subset of electricity-related and environmental factors, their respective window sizes, and the number of hidden neurons in ELM which leads to the best prediction accuracy.