Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Short Term Peak Demand Forecasting Using An Artificial Neural Network With Controlled Peak Demand Through Intelligent Electrical Loading
Download Short Term Peak Demand Forecasting Using An Artificial Neural Network With Controlled Peak Demand Through Intelligent Electrical Loading full books in PDF, epub, and Kindle. Read online Short Term Peak Demand Forecasting Using An Artificial Neural Network With Controlled Peak Demand Through Intelligent Electrical Loading ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Short-Term Peak Demand Forecasting Using an Artificial Neural Network with Controlled Peak Demand Through Intelligent Electrical Loading by : Jason L Grant
Download or read book Short-Term Peak Demand Forecasting Using an Artificial Neural Network with Controlled Peak Demand Through Intelligent Electrical Loading written by Jason L Grant and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The power output capacity of a local electrical utility is dictated by its customers' cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility's bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs), provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA), linear regression, and multivariate adaptive regression splines (MARSplines) and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE) of 3.9% as compared to the SMA, linear regression, and MARSplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME) of 18.2% as compared to the SMA, linear regression, and MARSplines AMEs of 26.2%, 45.1%, and 22.5% respectively.
Book Synopsis Short-Term Load Forecasting by Artificial Intelligent Technologies by : Wei-Chiang Hong
Download or read book Short-Term Load Forecasting by Artificial Intelligent Technologies written by Wei-Chiang Hong and published by MDPI. This book was released on 2019-01-29 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Book Synopsis Forecasting and Assessing Risk of Individual Electricity Peaks by : Maria Jacob
Download or read book Forecasting and Assessing Risk of Individual Electricity Peaks written by Maria Jacob and published by Springer Nature. This book was released on 2019-09-25 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general.
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 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 Short-Term Load Forecasting 2019 by : Antonio Gabaldón
Download or read book Short-Term Load Forecasting 2019 written by Antonio Gabaldón and published by MDPI. This book was released on 2021-02-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
Book Synopsis Hybrid Intelligent Technologies in Energy Demand Forecasting by : Wei-Chiang Hong
Download or read book Hybrid Intelligent Technologies in Energy Demand Forecasting written by Wei-Chiang Hong and published by Springer. This book was released on 2021-01-02 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies. It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory. The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.
Book Synopsis Short Term Load Forecasting Using Artificial Neural Networks by : Iskandar Kfoury
Download or read book Short Term Load Forecasting Using Artificial Neural Networks written by Iskandar Kfoury and published by . This book was released on 2015 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Short-term Electric Load Forecasting by Using Multi-layer Feed-forward Neural Network by : Marvin Herbert Wibisono
Download or read book Short-term Electric Load Forecasting by Using Multi-layer Feed-forward Neural Network written by Marvin Herbert Wibisono and published by . This book was released on 2004 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Short-term Electric Load Forecasting Using Artificial Neural Networks by : Eric Lee Daugherty
Download or read book Short-term Electric Load Forecasting Using Artificial Neural Networks written by Eric Lee Daugherty and published by . This book was released on 1994 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Integration of Demand Response into the Electricity Chain by : Arturo Losi
Download or read book Integration of Demand Response into the Electricity Chain written by Arturo Losi and published by John Wiley & Sons. This book was released on 2015-11-04 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of Demand Response (DR) generally concerns methodologies, technologies and commercial arrangements that could allow active participation of consumers in the power system operation. The primary aim of DR is thus to overcome the “traditional” inflexibility of electrical demand and, amongst others, create a new powerful tool to maximize deployment of renewable energy sources as well as provide active network management solutions to help reducing the impact of limited grid capabilities. DR allows consumers to actively participate in power system operation, thus bringing new opportunities in emerging energy markets as well as tangible system benefits. In this sense, DR is considered one of the key enablers of the Smart Grid concept. However, DR also poses a number of challenges, particularly when “active demand” is connected to the Low Voltage network, thus affecting all the actors involved in the electricity chain. This book presents for the first time a comprehensive view on technical methodologies and architectures, commercial arrangements, and socio-economic and regulatory factors that could facilitate the uptake of DR. The work is developed in a systematic way so as to create a comprehensive picture of challenges, benefits and opportunities involved with DR. The reader will thus be provided with a clear understanding of the complexity deriving from a demand becoming active, as well as with a quantitative assessment of the techno-economic value of the proposed solutions in a Smart Grid context. Many research contributions have appeared in recent years in the field of DR, both in journals and conference proceedings. However, most publications focus on individual aspects of the problem. A systematic treatment of the issues to be tackled to introduce DR in existing electricity grids, involving the extended value chain in terms of technical and commercial aspects, is still missing. Also, several books have recently been published about Smart Grid, in which there is some mention to DR. However, again while DR is seen as a key pillar for the Smart Grid, there is no dedicated, comprehensive and systematic contribution in this respect.
Book Synopsis Short-term Electric Load Forecasting Using Neural Network Models by : Yasser Al-Rashid
Download or read book Short-term Electric Load Forecasting Using Neural Network Models written by Yasser Al-Rashid and published by . This book was released on 1995 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forecasting and Assessing Risk of Individual Electricity Peaks by : Danica Vukadinovic Greetham
Download or read book Forecasting and Assessing Risk of Individual Electricity Peaks written by Danica Vukadinovic Greetham and published by . This book was released on 2020-10-08 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Book Synopsis Short Term Load Forecasting Using Artificial Neural Network by : Lawerence Chong
Download or read book Short Term Load Forecasting Using Artificial Neural Network written by Lawerence Chong and published by . This book was released on 1992 with total page 224 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 Power System Peak Load Forecasting Using an Artifical Neural Network by : John Cass
Download or read book Power System Peak Load Forecasting Using an Artifical Neural Network written by John Cass and published by . This book was released on 1996 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examines several models for short term load forecasting for the Rockhampton Area during the first six months of 1995. Artificial Neural Networks (ANN) were used.
Book Synopsis Electric Power Demand Forecasting Using Wavelets and Artificial Neural Networks by : Dirk Leo Hugen
Download or read book Electric Power Demand Forecasting Using Wavelets and Artificial Neural Networks written by Dirk Leo Hugen and published by . This book was released on 2001 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since wavelets represent an alternative way of representing the data to be analyzed, it gives an Artificial Neural Network a time-frequency dimension with which to correlate and model raw data. In short, Wavelet Neural Networks bridge the gap between near-sighted Artificial Neural Nets and far-sighted Trend/Periodic analysis of electric power demand. This thesis investigates the design and implementation of a Wavelet Neural Network for electric power demand forecasting. Several different wavelet basis functions are used to gain insight into how the Neural Net can use the time-frequency information contained in the wavelet coefficients. The Wavelet Neural Net is also tested in conjunction with known inputs to ascertain the effect of the wavelet coefficients on predictive capability for a known problem.
