Download or read book Unsupervised Learning for Residential Energy Consumption Analytics written by Thanchanok Teeraratkul and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Developing strategies to encourage reduction in households energy consumption requires utilities to categorize, predict and modify consumers electricity usage. Unfortunately, a typical consumer exhibits wide variation in daily 24-hour electricity usage patterns. Traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it difficult to classify consumers into stable representative groups and to predict individual electricity usage patterns. This dissertation presents two methods that better cluster consumer electricity usage pattern. The first method uses Dynamic time warping (DTW), which seeks an optimal alignment between electricity usage patterns. The second method assumes that electricity usage composes of a sequence of blocks generated from electrical devices. The clustering uses a novel block factorization model that embeds consumer usage pattern into a low dimensional space that flexibly captures time shifts of usage. Compared to commonly used clustering algorithm, both methods result in a more distinct set of clusters, and on average, a given consumer associates with a fewer clusters. The ideas and results from clustering is then used for individual electricity usage prediction. Prediction is done at two levels. The first level is day-to-day prediction where the shape of next day electricity usage pattern is predicted by cluster representatives from DTW. The second level is finer grain prediction based on block idea where load curve is predicted through block sequences. Predictions at both levels result in lower prediction error compared to some popular load forecasting techniques.

Download or read book Predictive Analytics for Energy Efficiency and Energy Retailing written by Konstantin Hopf and published by University of Bamberg Press. This book was released on 2019-07-15 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Mining and Machine Learning in Building Energy Analysis written by Frédéric Magoules and published by John Wiley & Sons. This book was released on 2016-02-08 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.

Download or read book Investigating the Human Behavior Side of Building Energy Efficiency written by Chao Chen and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We describe and evaluate each of these contributions using electricity consumption data from actual smart homes as part of the CASAS smart home project. In each case we illustrate the efficacy of these algorithms to gaining insights on human behavior and its impact on energy consumption, and offer ideas for using these insights to promote sustainable behaviors.

Download or read book Data Analytics for Renewable Energy Integration Technologies Systems and Society written by Wei Lee Woon and published by Springer. This book was released on 2018-11-16 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers from the 6th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2018, held in Dublin, Ireland, in September 2018. The 9 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response, and many others.

Download or read book Application of Big Data Deep Learning Machine Learning and Other Advanced Analytical Techniques in Environmental Economics and Policy written by Tsun Se Cheong and published by Frontiers Media SA. This book was released on 2022-07-25 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scalable Data driven Modeling and Analytics for Smart Buildings written by Srinivasan Iyengar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches - that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis - applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that automatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to report city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs.

Download or read book Towards Energy Smart Homes written by Stephane Ploix and published by Springer Nature. This book was released on 2021-11-11 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book exemplifies how smart buildings have a crucial role to play for the future of energy. The book investigates what already exists in regards to technologies, approaches and solutions both with a scientific and technological point of view. The authors cover solutions for mirroring and tracing human activities, optimal strategies to configure home settings, and generating explanations and persuasive dashboards to get occupants better committed in their home energy managements. Solutions are adapted from the fields of Internet of Things, physical modeling, optimization, machine learning and applied artificial intelligence. Practical applications are given throughout.

Download or read book Hybrid Artificial Intelligent Systems written by Pablo García Bringas and published by Springer Nature. This book was released on 2023-08-28 with total page 789 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2023, held in Salamanca, Spain, during September 5–7, 2023. The 65 full papers included in this book were carefully reviewed and selected from 120 submissions. They were organized in topical sections as follows: ​Anomaly and Fault Detection, Data Mining and Decision Support Systems, Deep Learning, Evolutionary Computation and Optimization, HAIS Applications, Image and Speech Signal Processing, Agents and Multiagents, Biomedical Applicatons.

Download or read book Data Science and Analytics written by Usha Batra and published by Springer Nature. This book was released on 2020-05-27 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.

Download or read book Proceedings of the International Conference on Artificial Intelligence and Computer Vision AICV2020 written by Aboul-Ella Hassanien and published by Springer Nature. This book was released on 2020-03-23 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Smart Trends in Computing and Communications written by Tomonobu Senjyu and published by Springer Nature. This book was released on with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nexus of Sustainability written by Anatoly Zagorodny and published by Springer Nature. This book was released on with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Python for Human Behavior Emotion and Health Status Analysis written by Md Zia Uddin and published by CRC Press. This book was released on 2024-08-30 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field. Key Features: Throughout the book, practicality takes precedence, with hands-on coding examples accompanying each concept to facilitate an interactive learning journey Striking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchers The content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalities Covering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications

Download or read book Machine Learning and the Internet of Things in Solar Power Generation written by Prabha Umapathy and published by CRC Press. This book was released on 2023-07-14 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book investigates various MPPT algorithms, and the optimization of solar energy using machine learning and deep learning. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental. This book: Discusses data acquisition by the internet of things for real-time monitoring of solar cells. Covers artificial neural network techniques, solar collector optimization, and artificial neural network applications in solar heaters, and solar stills. Details solar analytics, smart centralized control centers, integration of microgrids, and data mining on solar data. Highlights the concept of asset performance improvement, effective forecasting for energy production, and Low-power wide-area network applications. Elaborates solar cell design principles, the equivalent circuits of single and two diode models, measuring idealist factors, and importance of series and shunt resistances. The text elaborates solar cell design principles, the equivalent circuit of single diode model, the equivalent circuit of two diode model, measuring idealist factor, and importance of series and shunt resistances. It further discusses perturb and observe technique, modified P&O method, incremental conductance method, sliding control method, genetic algorithms, and neuro-fuzzy methodologies. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer, and environmental.

Download or read book Smart and Secure Embedded and Mobile Systems written by Jorge Marx Gómez and published by Springer Nature. This book was released on with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Knowledge is Power in Four Dimensions Models to Forecast Future Paradigm written by Bahman Zohuri and published by Academic Press. This book was released on 2022-07-14 with total page 1000 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigms, Forecasting Energy for Tomorrow's World with Mathematical Modeling and Python Programming Driven Artificial Intelligence delivers knowledge on key infrastructure topics in both AI technology and energy. Sections lay the groundwork for tomorrow's computing functionality, starting with how to build a Business Resilience System (BRS), data warehousing, data management, and fuzzy logic. Subsequent chapters dive into the impact of energy on economic development and the environment and mathematical modeling, including energy forecasting and engineering statistics. Energy examples are included for application and learning opportunities. A final section deliver the most advanced content on artificial intelligence with the integration of machine learning and deep learning as a tool to forecast and make energy predictions. The reference covers many introductory programming tools, such as Python, Scikit, TensorFlow and Kera. - Helps users gain fundamental knowledge in technology infrastructure, including AI, machine learning and fuzzy logic - Compartmentalizes data knowledge into near-term and long-term forecasting models, with examples involving both renewable and non-renewable energy outcomes - Advances climate resiliency and helps readers build a business resiliency system for assets