Download or read book A short term load forecasting model combining statistical and computational intelligence based models written by and published by . This book was released on 1906 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Este trabalho apresenta um novo modelo de previsão de curto prazo de carga elétrica que reúne técnicas de inteligência computacional e métodos estatísticos. Ele permite aproveitar as vantagens de inteligência computacional, relativas à criação de classes da série de entrada e ao processamento de variáveis climáticas de forma lingüística, e aquelas provenientes de modelos estatísticos, onde os parâmetros e a ordem do modelo são conhecidos e o intervalo de confiança das previsões é determinado. O modelo é uma extensão do método desenvolvido por P.C. Gupta, onde são empregadas técnicas de inteligência computacional junto com o método original. O modelo resultante compreende um classificador, um previsor e um procedimento para aprimorar as estimativas. O classificador é implementado por uma rede neural artificial com aprendizado não-supervisionado, enquanto o previsor emprega modelos estatísticos, combinando métodos de média móvel, amortecimento exponencial e auto-regressivo. Um sistema com lógica nebulosa utiliza variáveis climáticas no aprimoramento da previsão obtida.

Download or read book Short Term Load Forecasting by Artificial Intelligent Technologies written by Guo-Feng Fan and published by . This book was released on 2019 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

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

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.

Download or read book Load Forecasting on the User side by Means of Computational Intelligence Algorithms written by Juan José Cárdenas Araujo and published by . This book was released on 2014 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, it would be very difficult to deny the need to prioritize sustainable development through energy efficiency at all consumption levels. In this context, an energy management system (EMS) is a suitable option for continuously improving energy efficiency, particularly on the user side. An EMS is a set of technological tools that manages energy consumption information and allows its analysis. EMS, in combination with information technologies, has given rise to intelligent EMS (iEMS), which, aside from lending support to monitoring and reporting functions as an EMS does, it has the ability to model, forecast, control and diagnose energy consumption in a predictive way. The main objective of an iEMS is to continuously improve energy efficiency (on-line) as automatically as possible. The core of an iEMS is its load modeling forecasting system (LMFS). It takes advantage of historical information on energy consumption and energy-related variables in order to model and forecast load profiles and, if available, generator profiles. These models and forecasts are the main information used for iEMS applications for control and diagnosis. That is why in this thesis we have focused on the study, analysis and development of LMFS on the user side. The fact that the LMFS is applied on the user side to support an iEMS means that specific characteristics are required that in other areas of load forecasting they are not. First of all, the user-side load profiles (LPs) have a higher random behavior than others, as for example, in power system distribution or generation. This makes the modeling and forecasting process more difficult. Second, on the user side --for example an industrial user-- there is a high number and variety of places that can be monitored, modeled and forecasted, as well as their precedence or nature. Thus, on the one hand, an LMFS requires a high degree of autonomy to automatically or autonomously generate the demanded models. And on the other hand, it needs a high level of adaptability in order to be able to model and forecast different types of loads and different types of energies. Therefore, the addressed LMFS are those that do not look only for accuracy, but also adaptability and autonomy. Seeking to achieve these objectives, in this thesis work we have proposed three novel LMFS schemes based on hybrid algorithms from computational intelligence, signal processing and statistical theory. The first of them looked to improve adaptability, keeping in mind the importance of accuracy and autonomy. It was called an evolutionary training algorithm (ETA) and is based on adaptivenetwork-based-fuzzy-inference system (ANFIS) that is trained by a multi-objective genetic algorithm instead of its traditional training algorithm. As a result of this hybrid, the generalization capacity was improved (avoiding overfitting) and an easily adaptable training algorithm for new adaptive networks based on traditional ANFIS was obtained. The second scheme deals with LMF autonomy in order to build models from multiple loads automatically. Similar to the previous proposal, an ANFIS and a MOGA were used. In this case, the MOGA was used to find a near-optimal configuration for the ANFIS instead of training it. The LMFS relies on this configuration to work properly, as well as to maintain accuracy and generalization capabilities. Real data from an industrial scenario were used to test the proposed scheme and the multi-site modeling and self-configuration results were satisfactory. Furthermore, other algorithms were satisfactorily designed and tested for processing raw data in outlier detection and gap padding. The last of the proposed approaches sought to improve accuracy while keeping autonomy and adaptability. It took advantage of dominant patterns (DPs) that have lower time resolution than the target LP, so they are easier to model and forecast. The Hilbert-Huang transform and Hilbert-spectral analysis were used for detecting and selecting the DPs. Those selected were used in a proposed scheme of partial models (PM) based on parallel ANFIS or artificial neural networks (ANN) to extract the information and give it to the main PM. Therefore, LMFS accuracy improved and the user-side LP noising problem was reduced. Additionally, in order to compensate for the added complexity, versions of self-configured sub-LMFS for each PM were used. This point was fundamental since, the better the configuration, the better the accuracy of the model; and subsequently the information provided to the main partial model was that much better. Finally, and to close this thesis, an outlook of trends regarding iEMS and an outline of several hybrid algorithms that are pending study and testing are presented.

Download or read book Smart Meter Data Analytics written by Yi Wang and published by Springer Nature. This book was released on 2020-02-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Short Term Load Forecasting Using Computational Intelligence Methods written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This dissertation focuses on study of short term load forecasting using different types of computational intelligence methods. It uses evolutionary algorithms (i.e. Genetic Algorithm, Particle Swarm Optimization, Artificial Immune System), neural networks (i.e. MLPNN, RBFNN, FLANN, ADALIN, MFLNN, WNN, Recurrent NN, Wilcoxon NN), and fuzzy systems (i.e. ANFIS). The developed methods give load forecasts of one hour upto 24 hours in advance. The algorithms and networks were have been demonstrated using simulation studies. The power sector in Orissa has undergone various structural and organizational changes in recent past. The main focus of all the changes initiated is to make the power system more efficient, economically viable and better service oriented. All these can happen if, among other vital factors, there is a good and accurate system in place for forecasting the load that would be in demand by electricity customers. Such forecasts will be highly useful in proper system planning & operations. The techniques proposed in this thesis have been simulated using data obtained from State Load Dispatch Centre, Orissa for the duration September - 2006 to August - 2007.

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.

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.

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

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.

Download or read book Intelligent Optimization Modelling in Energy Forecasting written by Wei-Chiang Hong and published by MDPI. This book was released on 2020-04-01 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Download or read book ECML PKDD 2020 Workshops written by Irena Koprinska and published by Springer Nature. This book was released on 2021-02-01 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.

Download or read book Artificial Intelligence Based Forecasting and Analytic Techniques for Environment and Economics Management written by Wendong Yang and published by Frontiers Media SA. This book was released on 2022-11-09 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Computing and Intelligent Technologies written by Rabindra Nath Shaw and published by Springer Nature. This book was released on with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Lecture Notes in Data Engineering Computational Intelligence and Decision Making written by Sergii Babichev and published by Springer Nature. This book was released on 2022-09-13 with total page 735 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains of 39 scientific papers which include the results of research regarding the current directions in the fields of data mining, machine learning and decision-making. This book is devoted to current problems of artificial and computational intelligence including decision-making systems. Collecting, analysis and processing information are the current directions of modern computer science. Development of new modern information and computer technologies for data analysis and processing in various fields of data mining and machine learning create the conditions for increasing effectiveness of the information processing by both the decrease of time and the increase of accuracy of the data processing. The papers are divided in terms of their topic into three sections. The first section "Analysis and Modeling of Hybrid Systems and Processes" contains of 11 papers, and the second section "Theoretical and Applied Aspects of Decision-Making Systems" contains of 11 ones too. There are 17 papers in the third section "Data Engineering, Computational Intelligence and Inductive Modeling". The book is focused to scientists and developers in the fields of data mining, machine learning and decision-making systems.

Download or read book Computational Intelligence in Power Engineering written by Bijaya Ketan Panigrahi and published by Springer Science & Business Media. This book was released on 2010-09-20 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume deals with different computational intelligence (CI) techniques for solving real world power industry problems. It will be extremely helpful for the researchers as well as the practicing engineers in the power industry.