In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically.
Diploma III Electrical Technology Study Program is a institutional vocational education institutional in Department of Electrical Engineering and Informatics that aims to produce graduate who are ready to work in operation and maintenance of power system. Since phenomena of scarcity of fossil fuels, study program meet the 2 major problems, namely the limitations of electrical energy for practical lab work and increased job skills on the electrical energy conversion of electrical energy from renewable energy. The purpose of this research is to optimalize capacity of solar and wind energy contained in the environment of the laboratory on the microgrid configuration, namely PV-Wind turbine-Battery. Software HOMER is used to simulate microgrid configuration with AC-DC load, AC load, and DC load. The results show indicate that microgrid PV-Wind turbine-Battery is more economically to meet the need AC-DC load than the others.
– In a smart grid, the adequacy of electricity supply is not only determined by generation, but the electrical demand can also be involved. Demand response is one way to maintain a balance between electricity supply and load by reducing electricity consumption at a certain period. In this study, a microgrid system operating design is proposed by considering the penetration of new and renewable energy and demand response. Optimization is carried out with the aim of obtaining the lowest generation costs, while maximizing customer benefits from the demand response program. The mixed-integer linear programming method is used to determine generator generation and customer load reduction throughout the planning period. The obtained diesel generator operating cost is $116.40 and the total customer load response benefit is $100. Based on the analysis, the demand response is able to help the system maintain a power balance in critical conditions, namely when the supply from the generator is not sufficient. From the test system used, it was found that the load curtailment throughout the planning period is 67.04 kWh for three customers. The distribution of the reduced demand depends on the value of the demand response incentive for each customer. The amount of load reduction is strongly influenced by the specified demand response budget.Keywords – optimal dispatch, demand response, microgrid, mixed-integer linear programmingIntisari – Dalam smart grid, kecukupan pasokan listrik tidak hanya ditentukan oleh pembangkitan saja, tetapi beban listrik juga dapat dilibatkan. Respons beban merupakan salah satu cara untuk menjaga keseimbangan antara pasokan dan beban listrik dengan cara mengurangi pemakaian listrik pada waktu-waktu tertentu. Dalam studi ini diusulkan sebuah desain operasi sistem microgrid dengan mempertimbangkan penetrasi energi baru dan terbarukan serta respons beban. Optimisasi dilakukan dengan tujuan untuk memperoleh biaya pembangkitan terendah, sekaligus memaksimalkan keuntungan pelanggan dari program respons beban. Metode mixed-integer linear programming digunakan untuk menentukan pembangkitan pada generator dan pengurangan beban pelanggan sepanjang periode perencanaan. Biaya operasi pembangkit diesel yang diperoleh adalah $116,40 dan keuntungan respons beban yang diperoleh pelanggan sebesar $100. Berdasarkan analisis, respons beban mampu membantu sistem menjaga keseimbangan daya pada kondisi-kondisi kritis, yaitu ketika suplai dari pembangkit sedang tidak mencukupi. Dari sistem pengujian yang digunakan, diperoleh penurunan beban yang terjadi selama periode penjadwalan adalah 67,04 kWh pada tiga pelanggan. Distribusi beban yang dikurangi bergantung pada nilai insentif respons beban pada tiap-tiap pelanggan. Besarnya penurunan beban sangat dipengaruhi oleh anggaran respons beban yang ditetapkan.Kata kunci – penjadwalan pembangkit, respons beban, microgrid, pemrograman mixed-integer linear
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