Pembuatan Perangkat Keras dan Analisis Sub-Metering Konsumsi Energi Listrik

AbstrakPemantauan konsumsi energi merupakan langkah manajemen energi yang efektif dalam penghematan energi untuk jangka panjang. Pada penelitian ini dilakukan pengembangan pada perangkat keras dari sistem pemantauan untuk sub-metering. Sub-metering yang dilakukan membagi pengukuran menjadi tiga masukan arus: stop kontak, air conditioner, dan penerangan ambient, yang diakuisisi dengan menggunakan sensor arus non-invasive SCT-013-030. Data yang diakuisisi kemudian diolah oleh Arduino UNO, disimpan ke SD Card menggunakan Arduino Ethernet Shield dalam keadaan offline untuk kemudian dibuat model hariannya dari hasil pengukuran sebagai referensi pengukuran real-time.Untuk memverifikasi model harian dari hasil pengukuran harian pengguna energi, dibuat suatu pemodelan konsumsi listrik base-line yang diasumsikan hemat. Tampilan real-time yang nantinya dibandingkan dengan pemodelan harian diunggah ke jaringan Ethernet dengan menggunakan Arduino Ethernet Shield dalam keadaan online. Sedangkan perangkat lunak yang digunakan untuk menampilkan di halaman web adalah RGraph, sebuah perpustakaan grafik bebas pakai berbasis javascript.Pada akhirnya, penelitian ini dapat melahirkan rekomendasi-rekomendasi penghematan yang spesifik. Hasil penelitian ini juga menunjukkan bahwa sensitivitas sensor arus dan signal conditioner yang digunakan adalah sebesar 7 Ampere/bitdengan tingkat presisi yang baik. Sedangkan resolusi, yakni perubahan 1 bit pada signal conditioner terjadi apabila ada perubahan setiap 0, 15 Ampere pada sensor arus.Kata Kunci: Pemantauan Energi, Sub-metering, SCT-013-030, Arduino, Arduino Ethernet Shield, Real-time, RGrap

Perancangan Sistem Pemanenan Energi Surya Terintegrasi Kaca Bangunan, Studi Kasus: Gedung Bandar Lampung

In this paper, design of solar energy harvesting system which integrated in building glass window was proposed. The location as the design reference is Bandar Lampung Building. Bandar Lampung Building uses 90% of the glass on the outside walls building with facing the sunrise and sunset. The design solar energy harvesting system was consisted of solar glass and electronic power system. Solar glass using several mini PV affixed on the glass with space in between, so partially of sunlight pass into the room. The solar energy harvesting system used for DC house network and not connected to the grid system. The solar energy harvesting is also equipped with power electronic system such as MPPT, lead acid battery, and DC-DC converter. The design of solar energy harvesting system is using calculative method based on secondary data several references for this case. Area of the solar glass reaches 16.32 m2 for 1 office room scale. The ratio between PV and room glass about 0.35. The power average of the solar glass on the glass building with facing to the sunrise is about 74.35 W, and then the average power of the solar glass with facing to the sunlight about 161.32W

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