Browsing by Author "Falope, Tolulope Olumuyiwa"

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    A three-step weather data approach in solar energy prediction using machine learning
    (Elsevier, 2024-09) Falope, Tolulope Olumuyiwa; Lao, Liyun; Hanak, Dawid
    Solar energy plays a critical part in lowering CO2 emissions and other greenhouse gases when integrated into the grid. Higher solar energy penetration is hindered by its intermittency leading to reliability issues. To forecast solar energy production, this study suggests a three-step forecasting method that selects weather variables with a moderate to strong positive correlation to solar radiation using Pearson correlation coefficient analysis. Low-level data fusion is used to combine weather inputs from a reliable local weather station and an on-site weather station, significantly improving the forecasting model's accuracy regardless of the machine learning method used. Weather data was obtained from the Kisanhub Weather Station located in Cranfield University, UK and the meteorological station in Bedford, UK. In addition, PV power supply data was obtained from four solar plants. Using the Regression Learner app in MATLAB, the proposed architecture is tested on a utility scale solar plant (1 MW), showing a 6% and 13% prediction accuracy improvement when compared to solely using data from the on-site and local weather station respectively. It is further validated using data from three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root-mean square values of 0.0984, 0.0885, and 0.1425 respectively. The data was pre-processed using both rescaling and list-wise deletion methods. Training and testing data from the 1 MW solar plant was divided into 75% and 25% respectively, while 100% of the residential rooftop solar plants was used for validation.
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    ItemOpen Access
    An improved energy management system framework for solar energy integration.
    (Cranfield University, 2024-05) Falope, Tolulope Olumuyiwa; Lao, Liyun; Huo, Da
    Renewable energy sources like wind and solar play a crucial role in decarbonizing energy supply, but their variable and intermittent nature lead to reliability and stability issues. One way of sustainably integrating these energy sources into the grid is through an energy management system. The study reported in this thesis gives a comprehensive definition of an integrated energy management system and creates a novel framework that identifies energy forecasting, demand-side management, and supply-side management, as crucial components for grid balancing. In addition, this research looks particularly at solar integration, and how the integrated energy management system offers a unique combination of solar energy forecasting, time-of-use tariffs, direct load control demand response, and generator control, in increasing penetration levels of solar energy. The significance of this research is that the proposed system presents a viable, sustainable, and cheaper way of increasing PV usage and thereby grid penetration by prioritising efficient use of available PV supply before calling up additional supply. To validate the proposed integrated energy management system, this research looks to understand the functions of each individual component and how their interconnectedness creates a novel management system. Firstly, this research develops a three-step solar forecasting approach that uses low-level data fusion to combine weather variables from both an on-site and a local weather station to improve solar energy forecasting. The forecasting model response is historic PV generation, and the predictors are weather variables with moderate to strong positive correlations to solar radiation. Data obtained is preprocessed using Low-level Data Fusion, Pearson Correlation Coefficient analysis, Rescaling method, and List-wise Deletion method. This approach is then tested on a 1MW utility scale solar plant, resulting in a 6% and 13% prediction accuracy improvement when compared to solely using data from an on-site, and local weather stations respectively. This approach is also validated for three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root mean square error values of 0.0984, 0.1425, and 0.0885 respectively. The resulting low root mean square error values, a measure of the predicted PV to actual PV generation, proves that the model can be adopted for different PV plant sizes and is suitable for any customer across the distributed generation spectrum. To further improve the accuracy of the model, other preprocessing techniques are investigated and applied. The study shows that the combination of Low-level Data Fusion, Linear Interpolation, filling outliers, data smoothing, Rescaling method, moderate to strong PV correlation of weather parameters using Pearson Correlation Coefficient, day/time/month decomposition, seasonal decomposition, Principal Component Analysis, and holdout validation, increases the accuracy of the model by 75%. The ability of direct load control to manage energy consumption is validated in a case study by using Connected Power’s unique smart sockets and Lumen radio’s Mira Mesh Radio Frequency wireless network. Small plug-in loads were connected to ten smart sockets located in a robotics laboratory and a café, resulting in reduced energy consumption by 44% and 72% respectively when compared to the baseline without direct load control. Finally, the integrated energy management system framework is validated by testing its capacity to increase PV usage for an off-grid residential house with a PV/diesel generator power source. A decision-based algorithm is created that adjusts PV supply forecast errors, initiates direct load control responses to reduce excess load during periods of low PV supply, and/or increase power supply by calling up a diesel generator. In addition, this is combined with the proposed three-step solar energy forecasting approach and a programmable load schedule based on time-of-use criteria. The effects of customer behaviour are also analysed by using a 14% override rate, with 80% preconditioning and 20% rebounding. The hybrid PV/diesel generator power source with the proposed integrated energy management system is compared against two configurations: a baseline configuration that uses a solely diesel generator source, and a hybrid PV/diesel generator power source. Results show that the integrated energy management system reduced the lifetime expenditure costs and CO2 emissions by 44% and 46% respectively when compared to the baseline configuration, and by 8% and 9% in the hybrid photovoltaic/diesel generator, while also increasing the PV usage from this configuration by over 113%. This research also addresses opportunities and limitations of the proposed system and lays the foundation for future research using other intermittent renewable energy sources such as wind.
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    ItemOpen Access
    Development of an integrated energy management system for off-grid solar applications with advanced solar forecasting, time-of-use tariffs, and direct load control
    (Elsevier, 2024-06-19) Falope, Tolulope Olumuyiwa; Lao, Liyun; Huo, Da; Kuang, Boyu
    Effectively managing and maximizing the integration of renewable energy sources is essential for a sustainable power grid due to the stochastic and intermittent nature of renewable energy generation. This study develops a comprehensive Integrated Energy Management System incorporating supply-demand side management in the form of time-of-use credit, direct load control, and generator control to enhance photovoltaic utilization in off-grid applications. A novel three-step solar energy forecasting approach is proposed in this paper, utilizing low-level data fusion and regression models to predict next-day photovoltaic generation with improved accuracy, and a rule-based decision algorithm is developed to correct forecast errors and manage loads dynamically. A techno-economic analysis covering a 20-year duration is carried out for scenarios with and without the integrated energy management system; three configurations are investigated for supplying an off-grid residential home, including diesel generator, diesel generator/photovoltaic system, and diesel generator/photovoltaic system/integrated energy management system. Results reveal that the hybrid configuration with integrated energy management system achieved 44 % and 46 % reductions in costs and carbon dioxide emissions compared to the diesel generator alone, and 8 % and 9 % compared to the diesel generator/photovoltaic setup respectively. The Integrated Energy Management System further enhanced photovoltaic utilisation rate by over 113 % when compared to the diesel generator/photovoltaic system. Further evaluations include customer behaviour impacts, demonstrating that a fully automated system with 100 % compliance significantly outperforms systems with manual customer control, highlighting the detrimental effect of overrides on the efficiency of direct load control. The flexibility of the Integrated Energy Management System framework allows potential adaptation for on-grid applications, showcasing its utility in diverse operational contexts.