Engajamento de clientes da região de Florianópolis na adoção de medidores inteligentes

Relatório Técnico - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Produção CivilO desenvolvimento de políticas que promovam a adoção de medidores inteligentes é essencial para orientar a transição rumo ao uso sustentável de recursos como água, eletricidade e gás, bem como informar iniciativas de smart cities. Esta pesquisa explora as preferências das famílias em termos de diferentes medidores inteligentes e identifica os valores que as mesmas estão dispostas a pagar por diferentes configurações de medidores inteligentes para monitorar eletricidade, água e gás com base nas características de sua casa incluindo tipo de moradia, habitantes e valor da propriedade. Para tanto, foi utilizado o modelo denominado Mixed Multinomial Logit que considera a heterogeneidade nas preferências dos clientes por diferentes medidores. O modelo proposto é estimado em a uma pesquisa que incorpora um experimento de escolha discreta realizado com 232 respondentes na região metropolitana de Florianópolis. A abordagem utilizada oferece uma série de vantagens para facilitar a implementação mais ampla de sistemas de redes inteligentes que, de outra forma, seriam negligenciados usando abordagens tradicionais que dependem de estimativas agregadas de demanda e disposição para pagar por esquemas proposto

Engajamento de clientes da região de Florianópolis na adoção de medidores inteligentes

Relatório Técnico - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Produção Civil.O desenvolvimento de políticas que promovam a adoção de medidores inteligentes é essencial para orientar a transição rumo ao uso sustentável de recursos como água, eletricidade e gás, bem como informar iniciativas de smart cities. Esta pesquisa explora as preferências das famílias em termos de diferentes medidores inteligentes e identifica os valores que as mesmas estão dispostas a pagar por diferentes configurações de medidores inteligentes para monitorar eletricidade, água e gás com base nas características de sua casa incluindo tipo de moradia, habitantes e valor da propriedade. Para tanto, foi utilizado o modelo denominado Mixed Multinomial Logit que considera a heterogeneidade nas preferências dos clientes por diferentes medidores. O modelo proposto é estimado em a uma pesquisa que incorpora um experimento de escolha discreta realizado com 232 respondentes na região metropolitana de Florianópolis. A abordagem utilizada oferece uma série de vantagens para facilitar a implementação mais ampla de sistemas de redes inteligentes que, de outra forma, seriam negligenciados usando abordagens tradicionais que dependem de estimativas agregadas de demanda e disposição para pagar por esquemas proposto

Sistema de Clasificación de Inventarios basado en Algoritmos de Machine Learning

La gestión efectiva de inventarios es esencial para optimizar el control, almacenamiento y distribución de productos dentro de un sistema. En este estudio, se utilizó un enfoque basado en análisis estadístico y algoritmos de machine learning para determinar la clasificación óptima de ítems en el sistema de inventario de repuestos del sector automotriz. Para esto se examinó una base de datos que contenía las ventas de repuestos de una empresa automotriz a lo largo de un año. Mediante la aplicación de los algoritmos K-medias, Clustering Large Applications (CLARA) y Divisive Analysis (DIANA), se identificó una clasificación óptima distribuida en tres clústeres. Además, se realizó un análisis comparativo con la clasificación ABC para definir las características de cada agrupación. Los resultados demostraron que el algoritmo CLARA mejora la gestión de inventarios, permitiendo optimizar los espacios de almacenamiento, aumentar la eficiencia operativa, reducir costos, mejorar el servicio al cliente y tomar decisiones informadas. Se puede mencionar que, algunos productos destacados en las agrupaciones resultantes fueron el 2452084002, 5810159A00 y 3910045800 de las agrupaciones 1, 2 y 3 respectivamente; estos productos son relevantes debido a su total de ventas en cada agrupación relacionando su cantidad, costo y precio de venta. Este estudio contribuye al campo de la gestión de inventarios al demostrar cómo el uso de algoritmos de machine learning mediante análisis estadístico puede optimizar la clasificación de artículos en el inventario, siendo relevante en la toma de decisiones estratégicas mediante una distribución más precisa y adaptada a las necesidades de la empresa.Effective inventory management is essential to optimize the control, storage and distribution of products within a system. In this study, an approach based on statistical analysis and machine learning algorithms was used to determine the optimal classification of items in an automotive parts inventory system. For this purpose, a database containing the spare parts sales of an automotive company over the course of a year was examined. By applying the Kmeans, Clustering Large Applications (CLARA) and Divisive Analysis (DIANA) algorithms, an optimal classification distributed in three clusters was identified. In addition, a comparative analysis with the ABC classification was performed to define the characteristics of each cluster. The results showed that the CLARA algorithm improves inventory management, allowing to optimize storage space, increase operational efficiency, reduce costs, improve customer service and make informed decisions. It can be mentioned that, some outstanding products in the resulting clusters were 2452084002, 5810159A00 and 3910045800 from clusters 1, 2 and 3 respectively; these products are relevant due to their total sales in each cluster relating their quantity, cost and sales price. This study contributes to the field of inventory management by demonstrating how the use of machine learning algorithms through statistical analysis can optimize the classification of items in the inventory, being relevant in strategic decision making through a more accurate distribution adapted to the needs of the company.0000-0003-2154-327

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

Short-term forecast techniques for energy management systems in microgrid applications

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the global average temperature rise to less than 2°C. Achieving the set targets involves increasing energy efficiency and embracing cleaner energy solutions. Although advances in computing and Internet of Things (IoT) technologies have been made, there is limited scientific research work in this arena that tackles the challenges of implementing low-cost IoT-based Energy Management System (EMS) with energy forecast and user engagement for adoption by a layman both in off-grid or microgrid tied to a weak grid. This study proposes an EMS approach for short-term forecast and monitoring for hybrid microgrids in emerging countries. This is done by addressing typical submodules of EMS namely: load forecast, blackout forecast, and energy monitoring module. A short-term load forecast model framework consisting of a hybrid feature selection and prediction model was developed. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with the ‘linear Support Vector Machine (SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance than the individual regression models with a reduction in Mean Absolute Error (MAE) by 5.4%. In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and Random Forest (RF) model for short-term power outage prediction was proposed that predicted accurately almost half of the blackouts (49.16%), thereby performing slightly better than the stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart meter was developed to realize the implemented energy forecast and offer user engagement through monitoring and control of the microgrid towards the goal of increasing energy efficiency