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Regional Medium Term Hourly Electricity Demand Forecasting Based On Lstm Preprint
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Book Synopsis Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM: Preprint by :
Download or read book Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM: Preprint written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.
Book Synopsis Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM. by :
Download or read book Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM. written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below $5\%$ MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.
Book Synopsis Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast by : Federico Divina
Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Federico Divina and published by MDPI. This book was released on 2021-08-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Forecasting Methods for Management by : Steven C. Wheelwright
Download or read book Forecasting Methods for Management written by Steven C. Wheelwright and published by John Wiley & Sons. This book was released on 1977 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Outlines the full range of qualitative and quantitative forecasting methods. Discusses forecasting challenges, including learning the difference between explaining the past and predicting the future, and the impact of judgmental biases; and forecasting applications for short, medium, and long-term horizons. Annotation copyrighted by Book News, Inc., Portland, OR
Book Synopsis Zonal and Regional Load Forecasting in the New England Wholesale Electricity Market by : Jonathan T. Farland
Download or read book Zonal and Regional Load Forecasting in the New England Wholesale Electricity Market written by Jonathan T. Farland and published by . This book was released on 2013 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Power system planning, reliability analysis and economically efficient capacity scheduling all rely heavily on electricity demand forecasting models. In the context of a deregulated wholesale electricity market, using scheduling a region's bulk electricity generation is inherently linked to future values of demand. Predictive models are used by municipalities and suppliers to bid into the day-ahead market and by utilities in order to arrange contractual interchanges among neighboring utilities. These numerical predictions are therefore pervasive in the energy industry. This research seeks to develop a regression-based forecasting model. Specifically, electricity demand is modeled as a function of calendar effects, lagged demand effects, weather effects, and a stochastic disturbance. Variables such as temperature, wind speed, cloud cover and humidity are known to be among the strongest predictors of electricity demand and as such are used as model inputs. It is well known, however, that the relationship between demand and weather can be highly nonlinear. Rather than assuming a linear functional form, the structural change in these relationships is explored. Those variables that indicate a nonlinear relationship with demand are accommodated with penalized splines in a semiparametric regression framework. The equivalence between penalized splines and the special case of a mixed model formulation allows for model estimation with currently available statistical packages such as R, STATA and SAS. Historical data are available for the entire New England region as well as for the smaller zones that collectively make up the regional grid. As such, a secondary research objective of this thesis is to explore whether or not an aggregation of zonal forecasts might perform better than those produced from a single regional model. Prior to this research, neither the applicability of a semiparametric regression-based approach towards load forecasting nor the potential improvement in forecasting performance resulting from zonal load forecasting has been investigated for the New England wholesale electricity market.
Book Synopsis Regional and Residential Short Term Electric Demand Forecast Using Deep Learning by : Tareq Hossen
Download or read book Regional and Residential Short Term Electric Demand Forecast Using Deep Learning written by Tareq Hossen and published by . This book was released on 2018 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Recurrent Neural Networks for Short-Term Load Forecasting by : Filippo Maria Bianchi
Download or read book Recurrent Neural Networks for Short-Term Load Forecasting written by Filippo Maria Bianchi and published by Springer. This book was released on 2017-11-09 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Book Synopsis Electricity Demand Forecasting in a Changing Regional Context by : James Christopher Sapp
Download or read book Electricity Demand Forecasting in a Changing Regional Context written by James Christopher Sapp and published by . This book was released on 1987 with total page 1042 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forecasting Long-Term Electricity Demand Time Series Using Artificial Neural Networks by : Christian Behm
Download or read book Forecasting Long-Term Electricity Demand Time Series Using Artificial Neural Networks written by Christian Behm and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate forecast of electricity load are increasingly important. We present a method to forecast long-term weather-dependent hourly electricity load using artificial neural networks. The fully connected dense artificial neural networks with 5 hidden layers and 1,024 hidden nodes per layer are trained using historic data from 2006 to 2015. Input parameters comprise calendrical information, annual peak loads and weather data. The results are benchmarked against the method to forecast electric loads used in the current mid-term adequacy forecasts published by the European Network of Transmission System Operators (entso-e). For validation year 2016, our approach shows a mean absolute percentage error of 2.8%, whereas the common approach as used by entso-e shows an average error of 4.8% using peak load scaling. Further, we conduct forecasts for Germany, Sweden, Spain, and France for scenario year 2025 and assess parameter variations. Our approach can serve to increase prediction accuracy of future electricity loads.
Book Synopsis Integration of the State-level Electricity Demand Forecasting Model and the Regional Electricity Model by : Howarth E. Bouis
Download or read book Integration of the State-level Electricity Demand Forecasting Model and the Regional Electricity Model written by Howarth E. Bouis and published by . This book was released on 1983 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On Short-Term Load Forecasting Using Machine Learning Techniques by : Behnam Farsi
Download or read book On Short-Term Load Forecasting Using Machine Learning Techniques written by Behnam Farsi and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since electricity plays a crucial role in industrial infrastructures of countries, power companies are trying to monitor and control infrastructures to improve energy management, scheduling and develop efficiency plans. Smart Grids are an example of critical infrastructure which can lead to huge advantages such as providing higher resilience and reducing maintenance cost. Due to the nonlinear nature of electric load data there are high levels of uncertainties in predicting future load. Accurate forecasting is a critical task for stable and efficient energy supply, where load and supply are matched. However, this non-linear nature of loads presents significant challenges for forecasting. Many studies have been carried out on different algorithms for electricity load forecasting including; Deep Neural Networks, Regression-based methods, ARIMA and seasonal ARIMA (SARIMA) which among the most popular ones. This thesis discusses various algorithms analyze their performance for short-term load forecasting. In addition, a new hybrid deep learning model which combines long short-term memory (LSTM) and a convolutional neural network (CNN) has been proposed to carry out load forecasting without using any exogenous variables. The difference between our proposed model and previously hybrid CNN-LSTM models is that in those models, CNN is usually used to extract features while our proposed model focuses on the existing connection between LSTM and CNN. This methodology helps to increase the model's accuracy since the trend analysis and feature extraction process are accomplished, respectively, and they have no effect on each other during these processes. Two real-world data sets, namely "hourly load consumption of Malaysia" as well as "daily power electric consumption of Germany", are used to test and compare the presented models. To evaluate the performance of the tested models, root mean squared error (RMSE), mean absolute percentage error (MAPE) and R-squared were used. The results show that deep neural networks models are good candidates for being used as short-term prediction tools. Moreover, the proposed model improved the accuracy from 83.17\% for LSTM to 91.18\% for the German data. Likewise, the proposed model's accuracy in Malaysian case is 98.23\% which is an excellent result in load forecasting. In total, this thesis is divided into two parts, first part tries to find the best technique for short-term load forecasting, and then in second part the performance of the best technique is discussed. Since the proposed model has the best performance in the first part, this model is challenged to predict the load data of next day, next two days and next 10 days of Malaysian data set as well as next 7 days, next 10 days and next 30 days of German data set. The results show that the proposed model also has performed well where the accuracy of 10 days ahead of Malaysian data is 94.16\% and 30 days ahead of German data is 82.19\%. Since both German and Malaysian data sets are highly aggregated data, a data set from a research building in France is used to challenge the proposed model's performance. The average accuracy from the French experiment is almost 77\% which is reasonable for such a complex data without using any auxiliary variables. However, as Malaysian data and French data includes hourly weather data, the performance of the model after adding weather is evaluated to compare them before using weather data. Results show that weather data can have a positive influence on the model. These results show the strength of the proposed model and how much it is stable in front of some challenging tasks such as forecasting in different time horizons using two different data sets and working with complex data.
Book Synopsis Deep Learning for Electricity Forecasting Using Time Series Data by : Hanan Abdullah Alshehri
Download or read book Deep Learning for Electricity Forecasting Using Time Series Data written by Hanan Abdullah Alshehri and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Design of a Forecasting Model of Regional Electricity Consumption and Peak Demand by :
Download or read book Design of a Forecasting Model of Regional Electricity Consumption and Peak Demand written by and published by . This book was released on 1981 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Electrical Load Forecasting by : S.A. Soliman
Download or read book Electrical Load Forecasting written by S.A. Soliman and published by Elsevier. This book was released on 2010-05-26 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world’s foremost experts on the subject, Electrical Load Forecasting provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. The book begins with a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. The book is also supported by an online computer program that allows readers to construct, validate, and run short and long term models. Step-by-step guide to model construction Construct, verify, and run short and long term models Accurately evaluate load shape and pricing Creat regional specific electrical load models
Book Synopsis Statistics for Machine Learning by : Pratap Dangeti
Download or read book Statistics for Machine Learning written by Pratap Dangeti and published by Packt Publishing Ltd. This book was released on 2017-07-21 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Book Synopsis Design of a Forecasting Model of Regional Electricity Consumption and Peak Demand by : R. Olsen
Download or read book Design of a Forecasting Model of Regional Electricity Consumption and Peak Demand written by R. Olsen and published by . This book was released on 1981 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report develops and presents a plan of research for an integrated regional economic and electricity demand forecasting system. The system encompasses not only the forecasting of electricity sales to major classes of service and the forecasting of peak demands but also forecasting of regional economic and demographic growth that will substantially determine regional demand for electricity. Since the developments of regional electricity demand and regional economic activity models have proceeded quite independently of one another, the stte of the art of each is reviewed separately. Then, a research design for an integrated economic growth, electric energy, and peak load model is presented. This design is carefully planned to result in a national system of regional models that provides all of the necessary forecasting relationships to derive 25-year regional electric energy and peak load forecasts from national economic and demographic forecasts.
Book Synopsis The Economics of Climate Change by : Gary D. Libecap
Download or read book The Economics of Climate Change written by Gary D. Libecap and published by University of Chicago Press. This book was released on 2011-06-01 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: While debates over the consequences of climate change are often pessimistic, historical data from the past two centuries indicate many viable opportunities for responding to potential changes. This volume takes a close look at the ways in which economies—particularly that of the United States—have adjusted to the challenges climate change poses, including institutional features that help insulate the economy from shocks, new crop varieties, irrigation, flood control, and ways of extending cultivation to new geographic areas. These innovations indicate that people and economies have considerable capacity to acclimate, especially when private gains complement public benefits. Options for adjusting to climate change abound, and with improved communication and the emergence of new information and technologies, the potential for adaptation will be even greater in the future.
