Python Based Deep Learning Methods For Energy Consumption Forecasting
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Python Based Deep Learning Methods For Energy Consumption Forecasting
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Author : Josep Roman Cardell
language : en
Publisher:
Release Date : 2020
Python Based Deep Learning Methods For Energy Consumption Forecasting written by Josep Roman Cardell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
In a society where we do nothing but increase the use of electricity in our daily life, en-ergy consumption and the corresponding management is a major issue. The predictionof electric energy demand is a key component, for the power system operators, in themanagement of the electrical grid. The importance of forecasting a particular house-hold daily energy consumption does concern the end-user too, by reason of the designand sizing of a suitable renewable energy system and energy storage.The aim of this thesis is to develop and train a computing system capable of predict-ing, with best accuracy as possible, electricity consumption at household-level. Thispaper presents a Short Term Load Forecasting (STLF) with Artificial Neural Networks(ANN), which lead to accurate results in spite of the dwelling consumption unpre-dictability. The recorded data, containing the daily track of electricity consumption overa particular household from 2015 to 2018, was analysed. Subsequently, a study over theANN architecture and training algorithms was carried out in order to define a robustmodel. Furthermore, several experiments were conducted with different models, con-taining distinct inputs, aiming to compare the relevance of a diversity of parametersfor the network's training. Finally, the forecasting of the optimal models, created withthe insights collected over the whole research, was performed and compared in severalspecially selected time periods.The results showed how with the appropriate inputs and selection of hyperparame-ters, a shallow ANN can provide certain accuracy on the forecasting of electric energydemand. As well as a methodology to develop and train an artificial neural network.
Modern Time Series Forecasting With Python
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Author : Manu Joseph
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-24
Modern Time Series Forecasting With Python written by Manu Joseph and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-24 with Computers categories.
Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is for The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.
Advanced Optimization Methods And Big Data Applications In Energy Demand Forecast
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Author : Federico Divina
language : en
Publisher: MDPI
Release Date : 2021-08-30
Advanced Optimization Methods And Big Data Applications In Energy Demand Forecast written by Federico Divina and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-30 with Technology & Engineering categories.
The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.
Time Series Forecasting In Python
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Author : Marco Peixeiro
language : en
Publisher: Simon and Schuster
Release Date : 2022-10-04
Time Series Forecasting In Python written by Marco Peixeiro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-04 with Computers categories.
Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You'll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you're done, you'll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Digital Technologies And Applications
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Author : Saad Motahhir
language : en
Publisher: Springer Nature
Release Date : 2021-06-26
Digital Technologies And Applications written by Saad Motahhir and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-26 with Technology & Engineering categories.
This book gathers selected research papers presented at the First International Conference on Digital Technologies and Applications (ICDTA 21), held at Sidi Mohamed Ben Abdellah University, Fez, Morocco, on 29–30 January 2021. highlighting the latest innovations in digital technologies as: artificial intelligence, Internet of things, embedded systems, network technology, information processing, and their applications in several areas such as hybrid vehicles, renewable energy, robotic, and COVID-19. The respective papers encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
Time Series Forecasting Using Deep Learning
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Author : Ivan Gridin
language : en
Publisher: BPB Publications
Release Date : 2021-10-15
Time Series Forecasting Using Deep Learning written by Ivan Gridin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-15 with Computers categories.
Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks KEY FEATURES ● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts. ● Includes practical demonstration of robust deep learning prediction models with exciting use-cases. ● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence. DESCRIPTION This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch. The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task. Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques. WHAT YOU WILL LEARN ● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics. ● Learn the basics of neural architecture search with Neural Network Intelligence. ● Combine standard statistical analysis methods with deep learning approaches. ● Automate the search for optimal predictive architecture. ● Design your custom neural network architecture for specific tasks. ● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes. WHO THIS BOOK IS FOR This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed. TABLE OF CONTENTS 1. Time Series Problems and Challenges 2. Deep Learning with PyTorch 3. Time Series as Deep Learning Problem 4. Recurrent Neural Networks 5. Advanced Forecasting Models 6. PyTorch Model Tuning with Neural Network Intelligence 7. Applying Deep Learning to Real-world Forecasting Problems 8. PyTorch Forecasting Package 9. What is Next?
Deep Learning For Time Series Cookbook
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Author : Vitor Cerqueira
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-29
Deep Learning For Time Series Cookbook written by Vitor Cerqueira and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Computers categories.
Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes Key Features Learn the fundamentals of time series analysis and how to model time series data using deep learning Explore the world of deep learning with PyTorch and build advanced deep neural networks Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.What you will learn Grasp the core of time series analysis and unleash its power using Python Understand PyTorch and how to use it to build deep learning models Discover how to transform a time series for training transformers Understand how to deal with various time series characteristics Tackle forecasting problems, involving univariate or multivariate data Master time series classification with residual and convolutional neural networks Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs) Who this book is for If you’re a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.
Machine Learning For Time Series Forecasting With Python
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Author : Francesca Lazzeri
language : en
Publisher: John Wiley & Sons
Release Date : 2020-12-03
Machine Learning For Time Series Forecasting With Python written by Francesca Lazzeri and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-03 with Computers categories.
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
Deep Learning For Electricity Forecasting Using Time Series Data
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Author : Hanan Abdullah Alshehri
language : en
Publisher:
Release Date : 2021
Deep Learning For Electricity Forecasting Using Time Series Data written by Hanan Abdullah Alshehri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Electric power consumption categories.
The complexity and nonlinearities of the modern power grid render traditional physical modeling and mathematical computation unrealistic. AI and predictive machine learning techniques allow for accurate and efficient system modeling and analysis. Electricity consumption forecasting is highly valuable in energy management and sustainability research. Furthermore, accurate energy forecasting can be used to optimize energy allocation. This thesis introduces Deep Learning models including the Convolutional Neural Network (CNN), the Recurrent neural network (RNN), and Long Short-Term memory (LSTM). The Hourly Usage of Energy (HUE) dataset for buildings in British Columbia is used as an example for our investigation, as the dataset contains data from residential customers of BC Hydro, a provincial power utility company. Due to the temporal dependency in time-series observation data, data preprocessing is required before a model can be created. The LSTM model is utilized to create a predictive model for electricity consumption as output. Approximately 63% of the data is used for training, and the remaining 37% is used for testing. Various LSTM parameters are tested and tuned for best performance. Our LSTM predictive model can facilitate power companies’ resource management decisions.
Contribution Of Machine Learning To The Prediction Of Building Energy Consumption
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Author : Yuyao Chen
language : en
Publisher:
Release Date : 2023
Contribution Of Machine Learning To The Prediction Of Building Energy Consumption written by Yuyao Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
The ongoing energy transition, pivotal to mitigate global warming, could significantly benefit from advances in building energy consumption prediction. With the advent of big data, data-driven models are increasingly effective in forecasting tasks and machine learning is probably the most efficient method to build such predictive models nowadays. In this work, we provide a comprehensive review of machine learning techniques for forecasting, regarding preprocessing as well as state-of-the-art models such as deep neural networks. Despite the achievements of state-of-art models, accurately predicting high-fluctuation electricity consumption still remains a challenge. To tackle this challenge, we propose to explore two paths: the utilization of soft-DTW loss functions and the inclusion of exogenous variables. By applying the soft-DTW loss function with a residual LSTM neural network on a real dataset, we observed significant improvements in capturing the patterns of high-fluctuation load series, especially in peak prediction. However, conventional error metrics prove insufficient in adequately measuring this ability. We therefore introduce confusion matrix analysis and two new error metrics: peak position error and peak load error based on the DTW algorithm. Our findings reveal that soft-DTW outperforms MSE and MAE loss functions with lower peak position and peak load error. We also incorporate soft-DTW loss function with MSE, MAE, and Time Distortion Index. The results show that combining the MSE loss function performs the best and helps alleviate the problem of overestimated and sharp peaks problems occured. By adding exogenous variables with soft-DTW loss functions, the inclusion of calendar variables generally enhances the model's performance, particularly when these variables exhibit higher Pearson's correlation coefficients with the target variable. However, when the correlation between the calendar variables and the historical load patterns is relatively low, their inclusion has a negative impact on the model's performance. A similar relationship is observed with weather variables.
