Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques

Scientific community is currently doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. The main challenge lies in intelligently integrating the actions of all users connected to the grid. In this context, electricity load forecasting methodologies is a key component for demand-side management. This research compares the accuracy of different Machine Learning methodologies for the hourly energy forecasting in buildings. The main goal of this work is to demonstrate the performance of these models and their scalability for different consumption profiles. We propose a hybrid methodology that combines feature selection based on entropies with soft computing and machine learning approaches, he. Fuzzy Inductive Reasoning, Random Forest and Neural Networks. They are also compared with a traditional statistical technique ARIMA (Auto Regressive Integrated Moving Average). In addition, in contrast to the general approaches where offline modelling takes considerable time, the approaches discussed in this work generate fast and reliable models, with low computational costs. These approaches could be embedded, for instance, in a second generation of smart meters, where they could generate on-site electricity forecasting of the next hours, or even trade the excess of energy.Peer ReviewedPostprint (author's final draft

Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques

Scientific community is currently doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. The main challenge lies in intelligently integrating the actions of all users connected to the grid. In this context, electricity load forecasting methodologies is a key component for demand-side management. This research compares the accuracy of different Machine Learning methodologies for the hourly energy forecasting in buildings. The main goal of this work is to demonstrate the performance of these models and their scalability for different consumption profiles. We propose a hybrid methodology that combines feature selection based on entropies with soft computing and machine learning approaches, he. Fuzzy Inductive Reasoning, Random Forest and Neural Networks. They are also compared with a traditional statistical technique ARIMA (Auto Regressive Integrated Moving Average). In addition, in contrast to the general approaches where offline modelling takes considerable time, the approaches discussed in this work generate fast and reliable models, with low computational costs. These approaches could be embedded, for instance, in a second generation of smart meters, where they could generate on-site electricity forecasting of the next hours, or even trade the excess of energy.Peer Reviewe

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

International audienceAtrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF

IoT Gas Sensors Array for Unobtrusive Tracking of Cooking Activity

<p>Recent research on remote tracking environments has strengthened smart home IoT ecosystems by the integration of multiple sensing tools that capture not only contextual data in a private setting, but also information about its residents. This shift paves the way for remote health industries, as information traditionally out of reach is available 24/7. Gas sensing, moving away from privacy-invasive tracking paradigms, emerges within this context, inspiring the monitoring of activities of daily living (ADLs) that could facilitate the remote healthcare supervision of the elderly. In this paper, we present how a gas sensing array based on low-cost commercial metal oxide (MOX) gas sensors has been assembled for the development of Principal Component Analysis (PCA) model which detects cooking activity within a household. Our resulting unobtrusive tracking system requiring no user input and posing no privacy concerns, suitable to other ADL use cases, highlights how AI-equipped IoT cloud infrastructures and accurate gas sensors are called to revolutionise remote healthcare.</p&gt