2 research outputs found

    Audit Energi Menggunakan Intensitas Konsumsi Energi untuk Konservasi Energi di Gedung Kampus

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    Energy auditing is an essential step in optimizing energy use in commercial buildings. This research explores the application of energy auditing with the Energy Consumption Intensity method to improve energy efficiency in campus buildings. Considering the changes in occupancy and activity patterns in the university environment can provide a comprehensive insight into the associated energy consumption patterns. The audit analyzed the building's energy consumption and identified potential energy savings to improve energy efficiency. Energy data was collected and analyzed to evaluate the building's energy performance. Recommended energy conservation measures include updating the lighting system, optimizing the cooling system, and improving the efficiency of equipment use. This research recommends that campus building managers adopt sustainable practices in energy management, which can lead to reduced operational costs and lower environmental impacts. Thus, the energy audit approach with the IKE method is a relevant and effective strategy for achieving energy conservation goals in the university environment. Based on the analysis, the latest IKE for Labtek V is 38.01 (2021), while the IKE for Labtek VI is 16.75 (2021), showing inefficiency of energy use in both buildings inefficient.Audit energi merupakan langkah penting dalam optimisasi penggunaan energi di bangunan komersial. Penelitian ini mengeksplorasi penerapan audit energi dengan metode Intensitas Konsumsi Energi (IKE) untuk meningkatkan efisiensi energi di gedung kampus. Pertimbangan perubahan dalam pola hunian dan aktivitas di lingkungan universitas dapat memberikan wawasan menyeluruh terhadap pola konsumsi energi yang terkait. Audit dilakukan dengan menganalisis konsumsi energi gedung dan mengidentifikasi potensi penghematan energi guna meningkatkan efisiensi penggunaan energi. Data energi dikumpulkan dan dianalisis untuk mengevaluasi kinerja energi gedung. Langkah konservasi energi yang direkomendasikan mencakup pembaruan sistem pencahayaan, optimasi sistem pendinginan, dan peningkatan efisiensi penggunaan peralatan. Penelitian ini memberikan rekomendasi bagi pengelola gedung kampus dalam mengadopsi praktik-praktik berkelanjutan dalam manajemen energi, yang dapat mengurangi biaya operasional dan dampak lingkungan yang lebih rendah. Dengan demikian, pendekatan audit energi dengan metode IKE menjadi strategi yang relevan dan efektif dalam mencapai tujuan konservasi energi di lingkungan universitas. Berdasarkan analisis yang dilakukan diperoleh IKE terbaru untuk Labtek V adalah 38,01 (2021), sedangkan IKE untuk Labtek VI adalah 16,75 (2021). Hal ini mengartikan bahwa penggunaan energi pada kedua gedung tersebut belum efisien. Potensi penghematan dalam mengurangi pemakaian energi gedung didapatkan sebesar 6,6964%

    Enhancing the Reliability of Photovoltaic Systems in Microgrid at Campus Area

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    This paper assesses the reliability of photovoltaic systems within a microgrid, considering the system's operational mode and monthly data on solar radiation and load demand. The evaluation encompasses various reliability metrics, including microgrid failure rate, interruption duration, system unavailability, EENS, EIR, LOLE, and LOLP, with the objective of minimizing these parameters. The methodologies applied involve the Markov model and artificial intelligence algorithms such as Naive Bayes and Support Vector Machine (SVM). Results indicate that the microgrid exhibits enhanced reliability in an on-grid mode configuration, with a LOLP value of 0.0008. Furthermore, employing machine learning, specifically SVM, for LOLP calculation based on solar radiation yields a more precise value of 0.7245. This study offers valuable insights for policymakers and system designers in determining the optimal configuration for microgrids.This paper assesses the reliability of photovoltaic systems within a microgrid, considering the system's operational mode and monthly data on solar radiation and load demand. The evaluation encompasses various reliability metrics, including microgrid failure rate, interruption duration, system unavailability, EENS, EIR, LOLE, and LOLP, with the objective of minimizing these parameters. The methodologies applied involve the Markov model and artificial intelligence algorithms such as Naive Bayes and Support Vector Machine (SVM). Results indicate that the microgrid exhibits enhanced reliability in an on-grid mode configuration, with a LOLP value of 0.0008. Furthermore, employing machine learning, specifically SVM, for LOLP calculation based on solar radiation yields a more precise value of 0.7245. This study offers valuable insights for policymakers and system designers in determining the optimal configuration for microgrids
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