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.
Results
Showing 1 to 18 of 18 for “"Short-term load forecasting"”.
-
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 …
-
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 …
-
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 …
-
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 …
-
Comparative Analysis of Machine Learning Models for ERCOT Short Term Load Forecasting
… learning (ML) 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 …
-
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 …
-
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 …
-
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 …
-
Knowledge-based and statistical load forecast model development and analysis
… of the techniques 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 …
-
Graphon Mean Field Games with Finite States and Forecasting Models for the Energy Market
… with applied contributions to the modelling and forecasting of renewable energy systems. The first part, carried out in collaboration with Prof. Francesco Giuseppe Cordoni, focuses on the mathematical analysis of graphon mean field games (GMFGs), a generalisation of classical MFGs that allows for …
-
Integration of electric vehicles into power systems
… EV charging represents an intensive electric load. Their penetration into the power system poses significant challenges to the operation and control of the power distribution system. Therefore, grid operators need to prepare for high-level EV penetration into the power system. On the other …
-
The use of neural networks to help facilitate the accurate prediction of electricity demand on Crete
… carried out in order to implement a system for short-term load forecasting for the isolated power system of the island of Crete, Greece.<br/><br/>The system was based on the use of multilayer perceptron neural networks. Data used to train and test the networks were obtained from the Public Power …
-
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 …
-
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 …
-
Renewable Energy Integration in Distribution System with Artificial Intelligence
… the internal hidden information of the data. A short-term load forecasting approach is designed based on support vector regression (SVR) to provide a higher accuracy load forecasting for the network reconfiguration. The nonconvexity of three-phase balanced optimal power flow is relaxed to an …
-
Analyzing the temperature dimension of University of Illinois electricity demand
Made available in DSpace on 2016-05-04T21:49:06Z (GMT). No. of bitstreams: 2 GUERRERO-THESIS-2015.pdf: 2270398 bytes, checksum: 440c53b436df1200cd5e84da507ec2d7 (MD5) LICENSE.txt: 4213 bytes, checksum: fa95ff9e926f13b985b5f5e38e2af56e (MD5) Previous issue date: 2015-07-22
-
High-performance computing for smart grid analysis and optimization
… OPF computation. With the integration of intermittent renewable energy sources and demand response in the smart grid, there is increasing uncertainty involved in the traditional OPF problem. Therefore, probabilistic optimal power flow (POPF) analysis is required to accomplish the electrical …
-
The use of artificial intelligence techniques for power analysis
… for complex circuit arrangements such as multi-terminal circuits and composite overhead line and cable circuits. Also described, is how investigations have been made into an actual system fault that resulted in a failure of protection to operate. Techniques using digital fault records to replay …