Cost benefit analysis and data analytics for renewable energy and electrical energy storage

To accommodate with the global increase in the deployment of solar photovoltaic (PV) and energy storage system (ESS), a deterministic approach for sizing PV and ESS with anaerobic digestion biogas power plant; to meet a load demand will be presented in this plenary session. This aim is to maximize the sizing of PV to increase the security of energy supply. Energy economics for ESS will be a focus. Case study based on real-life data will be used to demonstrate the validity of the new approach

Cost benefit analysis and data analytics for renewable energy and electrical energy storage

EPSRC (Engineering and Physical Sciences Research Council) EP/P022049/

Interactive energy management for networked microgrids with risk aversion

Department of Finance and Education of Guangdong Province 2016[202]: Key Discipline Construction Programme, China; Guangdong Foshan Power Construction Corporation Group Co. Ltd., Foshan, China

Industrial data-driven monitoring based on incremental learning applied to the detection of novel faults

The detection of uncharacterized events during electromechanical systems operation represents one of the most critical data challenges dealing with condition-based monitoring under the Industry 4.0 framework. Thus, the detection of novelty conditions and the learning of new patterns are considered as mandatory competencies in modern industrial applications. In this regard, this article proposes a novel multifault detection and identification scheme, based on machine learning, information data-fusion, novelty-detection, and incremental learning. First, statistical time-domain features estimated from multiple physical magnitudes acquired from the electrical motor under inspection are fused under a feature-fusion level scheme. Second, a self-organizing map structure is proposed to construct a data-based model of the available conditions of operation. Third, the incremental learning of the condition-based monitoring scheme is performed adding self-organizing structures and optimizing their projections through a linear discriminant analysis. The performance of the proposed scheme is validated under a complete set of experimental scenarios from two different cases of study, and the results compared with a classical approach.Peer ReviewedPostprint (author's final draft

Análisis de sentimientos usando la red social Twitter ¿qué sintieron los turistas que volaron en 2020 con seleccionadas aerolíneas sudamericanas?

Activation of tourism is one of the key subjects for the airline industry. Internet contains a lot of information about tourists. This paper aims at analyzing the opinion of the tourists who traveled by certain South America airlines, using the sentiment analysis technique, employed in the study of their messages. The resource used for analysis is the information in twitter, provided by these airlines customers. First, a method for extracting published phrases related to target locations and "hashtags" was presented. Then, it was analyzed the polarity of the tweets extracted; creating positive, negative and eventually neutral opinions. In this process, there was utilized an unsupervised learning technique using seed words. The experimental result on the classification shows the efficacy of the applied method. Preliminary (descriptive) results as well as the basic proposal for a predictive model are herein attached.La activación del turismo es uno de los asuntos claves más importantes para la industria aeronáutica. Internet contiene mucha información sobre el turista. Analizar dicha información es una tarea significativa y desafiante. En este trabajo, se propone analizar la opinión de los turistas que viajaron en determinadas aerolíneas sudamericanas, mediante la técnica de análisis de sentimientos, a través del estudio de los mensajes de sus clientes. El recurso a utilizar para el análisis es la información en twitter, creada por clientes de determinadas dichas aerolíneas. Primero, se presenta un método para extraer las frases publicadas relacionadas con las ubicaciones de destino y los “hashtags”. Luego, se analizó la polaridad de los tweets extraídos; creando opiniones positivas, negativas y eventualmente neutras. Para el proceso, se empleó un enfoque de aprendizaje automático sin supervisión que utiliza palabras semilla. El resultado experimental sobre la clasificación muestra la eficacia del método aplicado. Se adjuntan los resultados preliminares (descriptivos) así como la propuesta base para un modelo predictivo

Autonomic management of a building's multi-HVAC system start-up

Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.European CommissionJunta de Comunidades de Castilla-La ManchaMinisterio de Ciencia e Innovació

Multi-view deep forecasting for hourly solar irradiance with error correction

Short-term solar irradiance forecasting is crucial in managing power network operations and solar photovoltaic applications. In this paper, a Multi-view Deep Forecasting method with Error Correction (MvDF_EC) for 1-hour ahead solar forecasting is proposed. MvDF_EC comprises of the Multi-view Deep Forecasting method (MvDF) and a robust Radial Basis Function Neural Network trained via minimizing the Localized Generalization Error for compensating the solar forecasting error of MvDF. MvDF consists of three deep neural networks which learn representations of input data from different views. The three views are 1) the hierarchical local temporal information extracted by the Temporal Convolutional Neural Network (TCN), 2) the key context sequential information captured by the Bi-directional Long Short-Term Memory Neural Network with Temporal Attention (BLSTMattn), and 3) long-term temporal dependencies between local temporal patterns filtered by the Convolutional Gated Recurrent Unit Neural Network (C_GRU). The solar forecasting performance of the proposed MvDF_EC is evaluated with the National Solar Radiation Database. Simulation results show that MvDF_EC yields the most accurate solar prediction compared with the benchmarks including the smart persistence and the state-of-the-art models. The lowest relative Root Mean Square Error values for Maraba and Labelle are 22.08% and 27.40%, respectively in 1-hour ahead solar forecasting.National Natural Science Foundation of China under Grants 61876066 and 61572201; Guangzhou Science and Technology Plan Project 201804010245; Department of Finance and Education of Guangdong Province 2016 [202] Key Discipline Construction Program, China; the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]; Brunel University London BRIEF Funding, UK

New Appliance Detection for Nonintrusive Load Monitoring

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty

Correlation analysis is one of the fundamental mathematical tools for identifying dependence between classes. However, the accuracy of the analysis could be jeopardized due to variance error in the data set. This paper provides a mathematical analysis of the impact of imbalanced data concerning Pearson Product Moment Correlation (PPMC) analysis. To alleviate this issue, the novel framework Robust Correlation Analysis Framework (RCAF) is proposed to improve the correlation analysis accuracy. A review of the issues due to imbalanced data and data uncertainty in machine learning is given. The proposed framework is tested with in-depth analysis of real-life solar irradiance and weather condition data from Johannesburg, South Africa. Additionally, comparisons of correlation analysis with prominent sampling techniques, i.e., Synthetic Minority Over-Sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) sampling techniques are conducted. Finally, K-Means and Wards Agglomerative hierarchical clustering are performed to study the correlation results. Compared to the traditional PPMC, RCAF can reduce the standard deviation of the correlation coefficient under imbalanced data in the range of 32.5%–93.02%