Global ETD Search
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Showing 1 to 18 of 18 for “"Energy forecasting"”.
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Service-Oriented Architecture based Cloud Computing Framework For Renewable Energy Forecasting
Forecasting has its application in various domains as the decision-makers are provided with a more predictable and reliable estimate of events that are yet to occur. Typically, a user would invest in licensed software or subscribe to a monthly or yearly plan in order to make such forecasts. The …
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Forecasting Energy Consumption using Sequence to Sequence Attention models
… conditions, reduce operating costs, and identify energy savings opportunities, it is essential to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely-used smart meters, have created possibilities for sensor based energy forecasting. Machine learning algorithms …
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Toward Transformer-based Large Energy Models for Smart Energy Management
Buildings contribute significantly to global energy demand and emissions, highlighting the need for precise energy forecasting for effective management. Existing research tends to focus on specific target problems, such as individual buildings or small groups of buildings, leading to current …
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Deep Learning Methods for Built Environment Operational Management
… anomaly detection (AD) and (ii) scalable energy forecasting. An unsupervised, univariate probabilistic anomaly detection framework—DEGAN: Density Estimation-based Generative Adversarial Networks (GANs)—was studied to enhance detection accuracy, with an emphasis on balancing the …
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Modelling Extreme Forecast Errors in Wind Energy Using South African Wind Farms
Accurate wind energy forecasting has become crucial for preserving grid stability and guaranteeing a consistent power supply in the light of South Africa’s expanding shift to renewable energy. As they have a direct impact on scheduling, dispatch choices, and reserve allocation, extreme prediction …
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Artificial Intelligence Forecasting Techniques For Reducing Uncertainties In Renewable Energy Applications
… for solar and wind farm operators and renewable energy regulators regarding factors influencing electricity production using those resources. The findings help production planning and grid stability improvements through better energy forecasting to reduce uncertainty. With the high increase in …
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Energy load prediction for Open Charge Point Protocol - compliant electric vehicle charging stations
… end-to-end data pipeline to enable short-term energy forecasting for individual EV charging stations. The system captures and processes OCPP transactions in PostgreSQL, deriving per-session energy for consistent forecasting targets. Using this platform, four models—Random Forest, K-Nearest …
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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 …
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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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Vertical Axis Hybrid Wind Turbine
… issue of VAWT. The development of sustainable energy sources has become increasingly critical in recent years because of growing worries about climate change, finite fossil fuel supplies, and the need for energy sustainability. Wind power stands out as a prominent and realistic alternative …
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Distribution Level Building Load Prediction Using Deep Learning
… grids is an important means to improve energy supply scheduling, reduce the production cost, and support emission reduction. Determining accurate load predictions has become more crucial than ever as electrical load patterns are becoming increasingly complicated due to the versatility of …
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Renewable Energy Integration in Distribution System with Artificial Intelligence
<p>With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power …
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Investigation of factors driving the costs of operating the 2020 Irish power system with large-scale wind generation
… This leads to issues resulting from wind energy being a non-synchronous, unpredictable and variable source of energy use on a scale never seen before for a single synchronous system. If changes are not made to traditional operational practices, the efficient running of the electricity …
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Historical Consistent Neural Networks for Wind Power Prediction
… during training that are unavailable during forecasting. Historical Consistent Neural Networks (HCNNs) offer a principled alternative by embedding observed and unobserved variables into a unified state, ensuring the dynamical rules evolve consistently across past and future. While originally …
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High-performance computing for smart grid analysis and optimization
… and control technologies, renewable energy sources, and machine learning, to improve the efficiency and reliability of existing electric power systems. The efficiency and reliability of power systems are of considerably importance to economic and environmental health in this new era. …
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Blockchain-based Peer-to-peer Electricity Trading Framework Through Machine Learning-based Anomaly Detection Technique
… installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy …
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Aplicación de machine learning para la eficiencia y predicción energética en microrredes colombianas
En este documento de investigación se analiza como los modelos de machine learning están siendo utilizados alrededor del mundo para la gestión energética de microrredes. Se realiza un estudio cualitativo de carácter exploratorio mediante la revisión de literatura científica. Las herramientas de …