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

Sistem Arsitektur Manajemen Bangunan untuk Memaksimalkan Swakonsumsi pada Bangunan Universitas: Studi Kasus

Due to its intermittent nature, significant adoption of solar PV into the grid can decrease grid reliability. One solution to increase it is to increase PV self-consumption with two methods: adding Energy Storage System (ESS) and conducting Demand Side Management (DSM). University building has a distinct characteristic in its complex dynamics. Therefore, there is a lack of research to control both methods of increasing self-consumption. This paper aimed to do an integrated literature review on increasing self-consumption and then propose a system architecture recommendation for university building management based on the review. The Smart Grid Architectural Model (SGAM) evaluated the case study object. The result showed that a data-driven controller has been chosen as the most suitable controller for the university building management system. The data needed to build a data-driven controller could be obtained through readily available sensors in the case study object, making it feasible for implementation.Dikarenakan sifatnya yang intermitten, adopsi energi dari PV surya ke dalam jaringan dapat mengurangi keandalan jaringan. Salah satu solusi untuk meningkatkannya adalah dengan meningkatkan swakonsumsi PV dengan dua metode: menambahkan Sistem Penyimpanan Energi (SPBE) dan melakukan manajemen sisi permintaan. Gedung universitas memiliki karakteristik yang berbeda dalam dinamika kompleksnya. Kurangnya penelitian untuk mengendalikan kedua metode ini di gedung-gedung universitas disebabkan oleh karakteristik ini. Makalah ini bertujuan untuk melakukan tinjauan literatur terintegrasi tentang upaya meningkatkan konsumsi sendiri kemudian mengusulkan rekomendasi arsitektur sistem untuk manajemen gedung universitas berdasarkan tinjauan tersebut. Kami kemudian mengevaluasi objek studi kasus menggunakan Smart Grid Architecture Model (SGAM). Hasilnya menunjukkan bahwa pengendali berbasis data telah dipilih sebagai pengendali yang paling cocok untuk sistem manajemen gedung universitas