Pengembangan Metodologi Prakiraan Beban Listrik Sektoral Secara Mikrospasial

Prakiraan kebutuhan beban listrik merupakan langkah penting dalam perencanaan pengembangan ketenagalistrikan yang dijabarkan dalam pola antisipasi untuk memenuhi kebutuhan energi listrik hingga jangka waktu tertentu. Metoda prakiraan beban sektoral saat ini lebih sederhana dan mudah untuk diimplementasikan, namun keakuratannya akan cenderung bias pada wilayah yang memiliki keterbatasan data dan area pelayanannya dinamis. Di samping itu hasil prakiraannya masih bersifat makro, sehingga tidak memperlihatkan pusat-pusat beban pada wilayah yang lebih kecil (grid) dan mengakibatkan lokasi gardu distribusi tidak dapat ditentukan dengan pasti. Dengan menggunakan metoda prakiraan beban sektoral secara mikrospasial akan menjadikan area yang diprediksi akan semakin banyak karena area berbentuk grid – grid, maka diperlukan pengelompokan grid (kelurahan) menggunakan teknik clustering untuk membuat similarity matrix yang memuat tingkat kemiripan antar data yang dikelompokkan. Clustering yang dilakukan melibatkan banyak faktor (multivariate)  yakni faktor geografi, demografi, sosio ekonomi dan beban kelistrikan per sektor. Hasil setiap cluster mempunyai karakteristik wilayah yang berbeda yang kemudian diproyeksikan pertumbuhan bebannya sehingga hasil prakiraan yang lebih teliti. Penelitian ini dilakukan di wilayah PT PLN (Persero) Area Tangerang. Dengan menggunakan metode mikrospasial didapatkan clusterisasi untuk pertumbuhan beban di PT PLN (Persero) area Tangerang sebanyak  5 cluster dari 114 keluraha

A systematic literature review using text mining and bibliometric analysis

109 “Consumo SMART” https://www.simplex.gov.pt/medidas. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.publishersversionpublishe

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape