Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 47 for “"load forecasting"”.
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Dynamic load forecasting for commercial power network
Load forecasting is an important component for power system energy management system. The electrical load is the power that an electric utility needs to supply in order to meet the demands of its customers. It is therefore very important to the utilities to have advance knowledge of their …
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Load forecasting for economic power system operation
Short-term load forecasting is important for reliable and economic operation of apower system. The aim of this research is the development of statistical models capable ofpredicting the short-term total system load for a small, isolated power system, utilising bothhistorical demand patterns and the …
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Elements of load forecasting and generation planning
The problems involved in load forecasting and long-term generation expansion have been discussed and techniques for load forecasting, reliability evaluation and optimal generation expansion analyzed. The results of a sample generation expansion plan using the Capacity Expansion and Reliability …
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Electric load forecasting with increased embedded renewable generation
Load forecasting remains a challenging problem in power system operation due to the growth in low carbon technologies and distributed small scale renewable generation. Addressing this challenge, this thesis explores of a number of load forecasting methodologies for short-term day-ahead load …
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Supervised Machine Learning Techniques for Short-Term Load Forecasting
<p>Electric Load Forecasting is essential for the utility companies for energy management based on the demand. Machine Learning Algorithms has been in the forefront for prediction algorithms. This Thesis is mainly aimed to provide utility companies with a better insight about the wide range of …
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A generalized rule-based short-term load forecasting technique
A newly-developed technique for short-term load forecasting is generalized. The algorithm combines features from knowledge-based and statistical techniques. The technique is based on a generalized model for the weather-load relationship, which makes it site independent. Weather variables are …
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A generalized ANN-based model for short-term load forecasting
Short-term load forecasting (STLF) deals with forecasting of hourly system demand with a lead time ranging from one hour to 168 hours. The basic objective of the STLF is to provide for economic, reliable and secure operation of the power system. This dissertation establishes a new approach to …
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Short-Term Load Forecasting Using Neural Network for Future Smart Grid Application
<p>Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised.</p> <p>This thesis outlines some aspects and functions of smart meter. It also …
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Comparative Analysis of Machine Learning Models for ERCOT Short Term Load Forecasting
… and deep learning (DL) models for short-term load forecasting (STLF) in the Electric Reliability Council of Texas (ERCOT) grid. A dual comparative approach is employed, evaluating models based on temporal features alone as well as in combination with actual and forecasted weather variables. …
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Symbolic and connectionist machine learning techniques for short-term electric load forecasting
… techniques to the problem of short-term electric load forecasting. The short-term electric load forecasting problem considered here is the prediction of bus loads one day ahead. The forecast quantities of interest are average integrated daily load and daily peak load. The primary objectives of …
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Short term load forecasting based on hybrid artificial neural networks and particle swarm optimisation
Short term load forecasting (STLF) is the prediction of electrical load for a period that ranges from the next minute to a week. The main objectives of the STLF function are to predict future load for the generation scheduling at power stations; assessment of the security of the power system as …
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An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models
Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly …
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A Comparative Study of Short-Term Electric Vehicle Load Forecasting Using Data-Driven Multivariate Probabilistic DeepAR Approach
… grid is facing many new challenges. Charging load forecasting remains one of the key challenges, that if not effectively scheduled, it may result in instability and quality-related issues in power systems. In recent years, numerous load forecasting techniques using machine learning and deep …
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An investigation into the application of artificial neural networks and cluster analysis in long-term load forecasting
… investigates the problem of electric long-tenn load forecasting based on weather conditions (specifically temperature) and also investigates load forecasting by segmenting customers according to their pattern of use using clustering techniques in order to produce an effective long-tenn load …
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Strategies for Managing Cool Thermal Energy Storage with Day-ahead PV and Building Load Forecasting at a District Level
In hot climate areas, the electrical load in a building spikes, but not by the same amount daily due to various conditions. In order to cover the hottest day of the year, large cooling systems are installed, but are not fully utilized during all hot summer days. As a result, the investments in …
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Knowledge-based and statistical load forecast model development and analysis
… that have been applied to the short-term load forecasting problem fall within the time series approaches. The exception to this has been a new approach based on the application of expert systems. Recently several techniques have been reported which apply the rule-based (or expert systems) …
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Assessment of geographical based load forecast approach in distribution planning
… 2007, Eskom Distribution followed a method of load forecasting (now referred to as legacy method in this report) that was based on collecting customer applications, historical load trending, and relied on the planner’s knowledge of the area to a large extent. It was based in a conventional …
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Uma metodologia para tratamento de dados de curvas de carga baseada em técnicas de inteligência artificial
Data quality is critical in the short-term load forecasting. Frequently, load data show aberrant values (outliers), discontinuities, and gaps (missing data) caused by the abnormal operation of the electrical system or failures and problems in the measurement system. The presence of corrupted data …
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Smart Grid Technologies and Implementations
… algorithms, artificial neural network (ANN) for load forecasting in large power system is proposed in this thesis and different learning methods of back-propagation, Quasi-Newton and Levenberg-Marquardt, are compared with each other to seek the best result in load forecasting. Bad load …
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