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Informasi mengenai penggunaan energi listrik merupakan salah satu elemen penting dalam hal pengaturan distribusi jaringan listrik pada jaringan pintar skala kecil (smart micro grid). Selain itu informasi pemakaian energi listrik dapat membantu konsumen melakukan proses evaluasi pemakaian energi listrik untuk menekan biaya tagihan pembayaran listrik yang secara tidak langsung berpengaruh pada efisensi energi keseluruhan. Salah satu metode dalam proses pemantauan pemakaian energi listrik adalah Non-Intrusive Load Monitoring (NILM). Permasalahan utama dalam NILM adalah mengetahui peralatan-peralatan elektronik yang ada dan mengetahui konsumsi energi listrik masing-masing peralatan dengan hanya melakukan proses pengambilan data hanya dari satu titik yang terhubung dengan semua peralatan elektronik pada jaringan listrik. Berdasarkan hasil pengujian menggunakan dataset AMPds dan REDD, nilai akurasi terendah yang didapatkan adalah sebesar 96,69% pada semua pengujian yang dilakukan.

 

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Information on electricity consumption is one of the essential elements in terms of regulating the distribution of electricity in smart micro grid. Besides, information on electricity consumption can help consumers carry out an evaluation process to reduce electricity bill costs, which indirectly affect overall energy efficiency. One method in the process of monitoring electricity consumption is Non-Intrusive Load Monitoring (NILM). The main problem in NILM is electronic disaggregation equipment that exists and determines the electrical energy consumption of each appliance by merely performing the retrieval of data from only one point connected with all the electronic devices on the electrical grid. Based on the results of tests conducted using the REDD and AMPds dataset, the lowest accuracy was 96.69% for all tests performed.

 

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"Hidden Markov models (HMMs) are discrete-state, discrete-time, stochastic dynamical systems. They are often used to approximate systems with continuous state spaces operating in continuous time. In addition to introducing the basic ideas of HMMs and algorithms for using them, this book explains the derivations of the algorithms with enough supporting theory to enable readers to develop their own variants. The book also presents Kalman filtering as an extension of ideas from basic HMMs to models with continuous state spaces.Although applications of HMMs have become numerous (396,000 Google hits) since they emerged as the key technology for speech recognition in the 1980s, no introductory book on HMMs in general is available. This text aims to fill that gap.Hidden Markov Models and Dynamical Systems features illustrations that use the Lorenz system, laser data, and natural language data. The concluding chapter presents the application of HMMs to detecting sleep apnea in experimentally measured electrocardiograms. Algorithms are given in pseudocode in the text, and a working implementation of each algorithm is available on the accompanying website."

"Tugas akhir ini secara umum bertujuan untuk menghadirkan suatu metode yang dapat membantu kita dalam menghitung nilai-nilai probabilitas transisi yang dibutuhkan dalam penghitungan aktuaria. Penghitungan nilai-nilai probabilitas transisi ini dibatasi pada model tiga state. Pembahasan model ini berkaitan erat dengan proses Markov dan menggunakan nilai force of transition konstan (Proses Markov waktu homogen). Metode yang digunakan dalam mencari nilai-nilai probabilitas transisi berangkat dari penggunaan matriks force of transition dengan force of transition yang bernilai konstan yaitu persamaan P(t) = Adiag( ed1t ,ed2t ,ed3t )A?1 dimana elemen-elemen matriks P(t) ialah nilai probabilitas transisi, vektor kolom dari matriks A ialah vektor eigen dari matriks force of transition, dan nilai d1, d2, d3 ialah nilai-nilai eigen dari matriks force of transition. Proses penghitungan nilai-nilai probabilitas ini melalui pencarian nilai eigen dan vektor eigen dari matriks force of transition. Dalam kasus khusus pada model khusus tiga state,yaitu state select, ultimate, dan dead, akan dibahas perhitungan numerik untuk mencari nilai-nilai probabilitas transisinya."

"This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models.All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. "