Household Electric Power Consumption Analysis Clustering And Prediction With Python

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Household Electric Power Consumption Analysis Clustering And Prediction With Python

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-03-03

Household Electric Power Consumption Analysis Clustering And Prediction With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-03 with Technology & Engineering categories.

In this project, you will perform analysis, clustering, and prediction on household electric power consumption with python. The dataset used in this project contains 2075259 measurements gathered between December 2006 and November 2010 (47 months). Following are the attributes in the dataset: date: Date in format dd/mm/yyyy; time: time in format hh:mm:ss; globalactivepower: household global minute-averaged active power (in kilowatt); globalreactivepower: household global minute-averaged reactive power (in kilowatt); voltage: minute-averaged voltage (in volt); global_intensity: household global minute-averaged current intensity (in ampere); submetering1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered); submetering2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light; and submetering3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner. In this project, you will perform clustering using KMeans to get 5 clusters. The machine learning models used in this project to perform regression on total number of purchase and to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.

Mastering Time Series Analysis And Forecasting With Python

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Author : Sulekha Aloorravi
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-03-26

Mastering Time Series Analysis And Forecasting With Python written by Sulekha Aloorravi and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-26 with Computers categories.

Decode the language of time with Python. Discover powerful techniques to analyze, forecast, and innovate. Key Features ● Dive into time series analysis fundamentals, progressing to advanced Python techniques. ● Gain practical expertise with real-world datasets and hands-on examples. ● Strengthen skills with code snippets, exercises, and projects for deeper understanding. Book Description "Mastering Time Series Analysis and Forecasting with Python" is an essential handbook tailored for those seeking to harness the power of time series data in their work. The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection. Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains. Readers develop expertise in crafting precise predictive models and addressing real-world complexities. Complete with illustrative examples, code snippets, and hands-on exercises, this manual empowers readers to excel, make informed decisions, and derive optimal value from time series data. What you will learn ● Understand the fundamentals of time series data, including temporal patterns, trends, and seasonality. ● Proficiently utilize Python libraries such as pandas, NumPy, and matplotlib for efficient data manipulation and visualization. ● Conduct exploratory analysis of time series data, including identifying patterns, detecting anomalies, and extracting meaningful features. ● Build accurate and reliable predictive models using a variety of machine learning and deep learning techniques, including ARIMA, LSTM, and CNN. ● Perform multivariate and multiple time series forecasting, allowing for more comprehensive analysis and prediction across diverse datasets. ● Evaluate model performance using a range of metrics and validation techniques, ensuring the reliability and robustness of predictive models. Table of Contents 1. Introduction to Time Series 2. Overview of Time Series Libraries in Python 3. Visualization of Time Series Data 4. Exploratory Analysis of Time Series Data 5. Feature Engineering on Time Series 6. Time Series Forecasting – ML Approach Part 1 7. Time Series Forecasting – ML Approach Part 2 8. Time Series Forecasting - DL Approach 9. Multivariate Time Series, Metrics, and Validation Index

Proceedings Of The International Conference On Artificial Intelligence And Computer Vision Aicv2020

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Author : Aboul-Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2020-03-23

Proceedings Of The International Conference On Artificial Intelligence And Computer Vision Aicv2020 written by Aboul-Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-23 with Technology & Engineering categories.

This book presents the proceedings of the 1st International Conference on Artificial Intelligence and Computer Visions (AICV 2020), which took place in Cairo, Egypt, from April 8 to 10, 2020. This international conference, which highlighted essential research and developments in the fields of artificial intelligence and computer visions, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into sections, covering the following topics: swarm-based optimization mining and data analysis, deep learning and applications, machine learning and applications, image processing and computer vision, intelligent systems and applications, and intelligent networks.

Progressive Computational Intelligence Information Technology And Networking

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Author : Poonam Nandal
language : en
Publisher: CRC Press
Release Date : 2025-07-22

Progressive Computational Intelligence Information Technology And Networking written by Poonam Nandal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Computers categories.

Progressive Computational Intelligence, Information Technology and Networking presents a rich and diverse collection of cutting-edge research, real-world applications, and innovative methodologies spanning across multiple domains of computer science, artificial intelligence, and emerging technologies. This comprehensive volume brings together different scholarly chapters contributed by researchers, practitioners, and thought leaders from around the globe. The book explores a wide array of topics including—but not limited to—machine learning, deep learning, cloud computing, cybersecurity, Internet of Things (IoT), blockchain, natural language processing, image processing, and data analytics. It addresses the practical implementation of technologies in sectors such as healthcare, agriculture, education, smart cities, environmental monitoring, finance, and more. Each chapter delves into specific challenges, frameworks, and experimental outcomes, making this book an essential reference for academicians, researchers, industry professionals, and students who aim to stay ahead in the rapidly evolving digital world.

Application Of Machine Learning And Deep Learning Methods To Power System Problems

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Author : Morteza Nazari-Heris
language : en
Publisher: Springer Nature
Release Date : 2021-10-20

Application Of Machine Learning And Deep Learning Methods To Power System Problems written by Morteza Nazari-Heris and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-20 with Technology & Engineering categories.

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.

Data Driven Analytics For Sustainable Buildings And Cities

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Author : Xingxing Zhang
language : en
Publisher: Springer Nature
Release Date : 2021-09-11

Data Driven Analytics For Sustainable Buildings And Cities written by Xingxing Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-11 with Social Science categories.

This book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.

Polygeneration Systems

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Author : Francesco Calise
language : en
Publisher: Academic Press
Release Date : 2021-09-22

Polygeneration Systems written by Francesco Calise and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Technology & Engineering categories.

The support for polygeneration lies in the possibility of integrating different technologies into a single energy system, to maximize the utilization of both fossil and renewable fuels. A system that delivers multiple forms of energy to users, maximizing the overall efficiency makes polygeneration an emerging and viable option for energy consuming industries. Polygeneration Systems: Design, Processes and Technologies provides simple and advanced calculation techniques to evaluate energy, environmental and economic performance of polygeneration systems under analysis. With specific design guidelines for each type of polygeneration system and experimental performance data, referred both to single components and overall systems, this title covers all aspects of polygeneration from design to operation, optimization and practical implementation. Giving different aspects of both fossil and non-fossil fuel based polygeneration and the wider area of polygeneration processes, this book helps readers learn general principles to specific system design and development through analysis of case studies, examples, simulation characteristics and thermodynamic and economic data. - Detailed economic data for technology to assist developing feasibility studies regarding the possible application of polygeneration technologies - Offers a comprehensive list of all current numerical and experimental results of polygeneration available - Includes simulation models, cost figures, demonstration projects and test standards for designers and researchers to validate their own models and/or to test the reliability of their results

Ai And Learning Systems

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Author : Konstantinos Kyprianidis
language : en
Publisher: BoD – Books on Demand
Release Date : 2021-02-17

Ai And Learning Systems written by Konstantinos Kyprianidis and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-17 with Technology & Engineering categories.

Over the last few years, interest in the industrial applications of AI and learning systems has surged. This book covers the recent developments and provides a broad perspective of the key challenges that characterize the field of Industry 4.0 with a focus on applications of AI. The target audience for this book includes engineers involved in automation system design, operational planning, and decision support. Computer science practitioners and industrial automation platform developers will also benefit from the timely and accurate information provided in this work. The book is organized into two main sections comprising 12 chapters overall: •Digital Platforms and Learning Systems •Industrial Applications of AI

Smart Meter Data Analytics

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Author : Yi Wang
language : en
Publisher: Springer Nature
Release Date : 2020-02-24

Smart Meter Data Analytics written by Yi Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-24 with Business & Economics categories.

This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.

Applying Predictive Analytics

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Author : Richard V. McCarthy
language : en
Publisher: Springer
Release Date : 2019-03-12

Applying Predictive Analytics written by Richard V. McCarthy and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-12 with Technology & Engineering categories.

This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes.