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

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%

Peningkatan Kinerja Microgrid Bangunan Kampus dengan Simulasi Multi Skenario dan Analisis Sensitivitas

Penelitian ini mengevaluasi kinerja microgrid cerdas dengan tujuan untuk meningkatkan ketersediaan pasokan listrik dan renewable fraction (RF). Evaluasi dilakukan dengan simulasi multi skenario yang mencakup produksi dan konsumsi energi. Simulasi dibagi tiga, yaitu skenario dasar, skenario uji, dan skenario rekomendasi. Skenario uji terdiri dari uji kapasitas sistem, penggantian komponen, dan analisis sensitivitas. Didapatkan dari skenario dasar bahwa ketersediaan pasokan listrik selama setahun telah terpenuhi, dengan RF 30,5%; cost of energy (CoE) Rp2.019/kWh; dan waktu otonomi baterai (WOB) 11,1 jam. Dari hasil analisis didapatkan beberapa rekomendasi berupa penggantian komponen baterai dan modul surya, penambahan kapasitas baterai, dan pengaturan batas state of charge (SoC) pada baterai untuk meningkatkan RF. Skenario rekomendasi tersebut berhasil meningkatkan ketersediaan pasokan listrik dan mencapai target dengan nilai WOB sebesar 37 jam dan RF sebesar 46,4% pada awal siklus hidup proyek; serta WOB sebesar 25,5 jam dan RF sebesar 29,1% pada akhir tahun ke 25, dengan CoE sebesar Rp6.448/kWh. Analisis sensitivitas operasi baterai lead-acid menunjukkan bahwa untuk mendapatkan RF maksimal rentang pengaturan SoC berada pada 0-20%. Sedangkan untuk baterai Li-Ion, rentang SoC adalah 0-25%

Enhancing the Reliability of Photovoltaic Systems in Microgrid at Campus Area

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

Data Driven Building Electricity Consumption Model Using Support Vector Regression

Every building has certain electricity consumption patterns that depend on its usage. Building electricity budget planning requires a consumption forecast to determine the baseline electricity load and to support energy management decisions. In this study, an algorithm to model building electricity consumption was developed. The algorithm is based on the support vector regression (SVR) method. Data of electricity consumption from the past five years from a selected building object in ITB campus were used. The dataset unexpectedly exhibited a large number of anomalous points. Therefore, a tolerance limit of hourly average energy consumption was defined to obtain good quality training data. Various tolerance limits were investigated, that is 15% (Type 1), 30% (Type 2), and 0% (Type 0). The optimal model was selected based on the criteria of mean absolute percentage error (MAPE) < 20% and root mean square error (RMSE) < 10 kWh. Type 1 data was selected based on its performance compared to the other two. In a real implementation, the model yielded a MAPE value of 14.79% and an RMSE value of 7.48 kWh when predicting weekly electricity consumption. Therefore, the Type 1 data-based model could satisfactorily forecast building electricity consumption

Pengembangan Pengontrol Tegangan Sistem Mikrogrid Cerdas Menggunakan Sistem Baterai Penyimpan Energi

A power outage on a conventional grid can cut the electricity supply to the entire load.  In contrast, Microgrid (MG) can still supply at least the most critical local loads even though blackout occurs in the main grid. MG can also utilize renewable energy sources such as solar and wind energy to generate electricity. That is possible by the advancement of the battery energy storage system (BESS). The BESS able to maintains electricity supply to the load even in outages. The inverter on the SBPE also plays a role in stabilizing the MG output voltage by supplying or absorbing reactive power in the MG system. This paper focuses on the control development of the battery inverter primary controller. The droop control design utilizes the deadband around the nominal voltage. That becomes the improvement of the droop control method used in this study compared to the initial formulation of the droop method. The proposed method was then tested through simulation with four different scenarios. The BESS will operate in the voltage range 194.9V to 234.6V with a droop control deadband in the voltage range 198.0V to 231.0V. Based on the simulation results, the addition of SBPE with the MG scheme on the existing system can improve the quality of the voltage received by the load from 0.994p.u. to 0.997p.u. The simulation also shows that the load still gets a power supply even though there is a blackout on the main grid

Simulasi Energi dan Keekonomian Sistem Pembangkit Listrik Tenaga Surya (PLTS) untuk Fungsi Peak Load Shaving pada Bangunan di Lingkungan Kampus ITB

Pada paper ini, dilakukan simulasi produksi energi dan keekonomian dari sistem PLTS terintegrasi dengan jaringan listrik yang merupakan bagian dari proyek instalasi sistem smart microgrid di bangunan Center for Advanced Sciences (CAS) ITB. Terdapat dua sistem PLTS yang dianalisis yaitu sistem PLTS 40 kWp tanpa baterai, dan sistem PLTS 10 kWp dengan baterai yang terhubung ke beban kritis. Kedua sistem PLTS tersebut dirancang dengan tujuan peak load shaving yang dapat meminimalisir adanya ketidakseimbangan antara produksi energi surya dan permintaan listrik. Kedua sistem dianalisis dengan metoda simulasi berdasarkan aspek energi dan ekonomi menggunakan perangkat lunak PVsyst dan Homer. Dari hasil simulasi sistem PLTS 40 kWp didapatkan nilai performance ratio (PR) sebesar 0,833, renewable fraction (RF) sebesar 18,73%, dan cost of energy (COE) sebesar Rp 1.251,85/kWh yang mana nilai PR dan COE telah memenuhi target bisnis: PR > 0.8 dan COE < Rp 1.467,28/kWh tetapi tidak memenuhi target RF > 35%. Sementara dari hasil simulasi sistem PLTS 10 kWp didapatkan nilai PR sebesar 0,77, RF sebesar 44,38% hingga 52,19% pada rentang depth of discharge 20% - 80%, dan COE sebesar Rp 2.103/kWh hingga Rp 6.315/kWh pada rentang DoD 20% - 80% yang mana hanya nilai RF telah memenuhi target bisnis

Development of Non-Intrusive Load Monitoring of Electricity Load Classification with Low-Frequency Sampling Based on Support Vector Machine

Non-intrusive load monitoring (NILM) is a promising approach to provide energy consumption monitoring of electrical appliances and analysis of current and voltage data with less instrumentation. This paper proposes an electrical load classification model using support vector machine (SVM). SVM was chosen to keep the computational cost low and be able to implement an embedded system. The SVM model was utilized to classify the on/off state of air conditioners, light bulbs, other uncategorized electronics, and their combinations. It utilizes low-frequency sampling data captured every minute, or at a 0.0167 Hz rate. Utilization change in active and reactive power was used as a feature in the model training. The optimal kernel for the model was the radial basis function (RBF) kernel with C and gamma values of 88.587 and 2.336 as hyperparameters, producing a highly accurate model. In testing with real-time conditions, the model classified the on/off state of the electrical loads with 0.93 precision, 0.91 recall, and 0.91 f-score. The results of testing proved that the model can be applied in real time with high accuracy and with an acceptable performance in field implementation using an embedded system

Development of Non-Intrusive Load Monitoring of Electricity Load Classification with Low-Frequency Sampling Based on Support Vector Machine

Non-intrusive load monitoring (NILM) is a promising approach to provide energy consumption monitoring of electrical appliances and analysis of current and voltage data with less instrumentation. This paper proposes an electrical load classification model using support vector machine (SVM). SVM was chosen to keep the computational cost low and be able to implement an embedded system. The SVM model was utilized to classify the on/off state of air conditioners, light bulbs, other uncategorized electronics, and their combinations. It utilizes low-frequency sampling data captured every minute, or at a 0.0167 Hz rate. Utilization change in active and reactive power was used as a feature in the model training. The optimal kernel for the model was the radial basis function (RBF) kernel with C and gamma values of 88.587 and 2.336 as hyperparameters, producing a highly accurate model. In testing with real-time conditions, the model classified the on/off state of the electrical loads with 0.93 precision, 0.91 recall, and 0.91 f-score. The results of testing proved that the model can be applied in real time with high accuracy and with an acceptable performance in field implementation using an embedded system

Modelling and Identification of Oxygen Excess Ratio of Self-Humidified PEM Fuel Cell System

One essential parameter in fuel cell operation is oxygen excess ratio which describes comparison between reacted and supplied oxygen number in cathode. Oxygen excess ratio relates to fuel cell safety and lifetime. This paper explains development of air feed model and oxygen excess ratio calculation in commercial self-humidified PEM fuel cell system with 1 kW output power. This modelling was developed from measured data which was limited in open loop system. It was carried out to get relationship between oxygen excess ratio with stack output current and fan motor voltage. It generated fourth-order 56.26% best fit ARX linear polynomial model estimation (loss function = 0.0159, FPE = 0.0159) and second-order ARX nonlinear model estimation with 75 units of wavenet estimator with 84.95% best fit (loss function = 0.0139). The second-order ARX model linearization yielded 78.18% best fit (loss function = 0.0009, FPE = 0.0009)