2,891 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Research on a price prediction model for a multi-layer spot electricity market based on an intelligent learning algorithm

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    With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region

    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

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    Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures

    Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

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    The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The results showed that 152 documents have been published on the topic comprising conference proceedings (58.6%) and articles (41.4%) between 2004 and 2022. Publication trends analysis revealed the number of publications increased from 1 to 31 or by 3,000% over the same period, which was ascribed to the growing scientific interest and research impact of the topic. Stakeholder analysis revealed the top authors/researchers are Anvari M, Ghaderi SF and Saberi M, whereas the most prolific affiliation and nations actively engaged in the topic are the North China Electric Power University, and China, respectively. Conversely, the top funding agency actively backing research on the topic is the National Natural Science Foundation of China (NSFC). Co-authorship analysis revealed high levels of collaboration between researching nations compared to authors and affiliations. Hotspot analysis revealed three major thematic focus areas namely; Energy Grid Forecasting, Power Generation Control, and Intelligent Energy Optimization. In conclusion, the study showed that the application of ACTs in EPG is an active, multidisciplinary, and impact area of research with potential for more impactful contributions to research and society at large

    Intelligent Solar Forecasts: Modern Machine Learning Models & TinyML Role for Improved Solar Energy Yield Predictions

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    The advancement of sustainable energy sources necessitates the development of robust forecasting tools for efficient energy management. A prominent player in this domain, solar power, heavily relies on accurate energy yield predictions to optimize production, minimize costs, and maintain grid stability. This paper explores an innovative application of tiny machine learning to provide real-time, low-cost forecasting of solar energy yield on resource-constrained edge internet of things devices, such as micro-controllers, for improved residential and industrial energy management. To further contribute to the domain, we conduct a comprehensive evaluation of four prominent machine learning models, namely unidirectional long short-term memory, bidirectional gated recurrent unit, bidirectional long short-term memory, and simple bidirectional recurrent neural network, for predicting solar farm energy yield. Our analysis delves into the impacts of tuning the machine learning model hyperparameters on the performance of these models, offering insights to improve prediction accuracy and stability. Additionally, we elaborate on the challenges and opportunities presented by the implementation of machine learning on low-cost energy management control systems, highlighting the benefits of reduced operational expenses and enhanced grid stability. The results derived from this study offer significant implications for energy management strategies at both household and industrial scales, contributing to a more sustainable future powered by accurate and efficient solar energy forecasting

    Weather and climate data for energy applications

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    Weather information plays a critical role in energy applications — from designing and planning to the management and maintenance of building energy systems, renewable energy applications, and smart utility grids. This research examines weather and climate data for energy applications, covering their sources, generation, implementation, and forecasting. Drivers for the use of weather data, data acquisition methods, and parameter characteristics, as well as their impact on energy applications, are critically reviewed. The study also analyses weather data availability from 32 commonly used online sources, considering their cost, features, and resolution. A comprehensive weather data classification is developed based on measurement type, information period, data resolution, and time horizon. The findings indicate that real-time local weather data with high temporal resolution is crucial for optimal energy management and accurate forecasting of energy and environmental behaviours. However, limitations and uncertainties exist in weather data from online sources, particularly for developing countries, due to the limited spatio-temporal coverage

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
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