Model Fuzzy Linear Regression Untuk Peramalan Produksi Kelapa Sawit (Studi Kasus: PT. Perkebunan III Medan)

Peramalan terhadap produksi kelapa sawit sudah sering dilakukan dengan berbagai pendekatan. Namun bukan hal yang mudah mencapai akurasi peramalan data jangka panjang dengan ukuran kecil. Penelitian ini bertujuan untuk menentukan model peramalan produksi kelapa sawit dengan metode Fuzzy Linear Regression (FLR). Metode tersebut dibentuk menggunakan triangular fuzzy number simetris berdasarkan standar deviasi data. Dalam penelitian ini, digunakan data bulanan jumlah produksi kelapa sawit sebagai variabel terikat (Y) dan faktor-faktor yang mempengaruhinya yaitu pemupukan (X1), tenaga kerja (X2) dan rata-rata curah hujan (X3) mulai dari tahun 2011-2012. Hasil analisa menunjukkan bahwa model peramalan terbaik yaitu dengan pendekatan metode FLR tanpa konstanta untuk jumlah produksi yait

Model Peramalan Distribusi Listrik Menggunakan Fuzzy Linear Regression (Studi Kasus: Sektor Rumah Tangga)

Semakin besarnya kebutuhan energi listrik terutama disektor rumah tangga mengakibatkan semakin bertambahnya distribusi listrik yang harus dipenuhi oleh PLN Indonesia. Sementara model peramalan yang akurat sangat diperlukan untuk distribusi listrik tersebut. Tujuan penulisan artikel ini adalah untuk menentukan model peramalan distribusi listrik sektor rumah tangga menggunakan fuzzy linear regression. Pembentukan model diawali dengan transformasi data single point menjadi bentuk triangular fuzzy number simetris berdasarkan aturan Sturges. Data yang digunakan merupakan data sekunder dari BPS Indonesia dari tahun 2000-2015. Variabel distribusi listrik sektor rumah tangga sebagai variabel terikat

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and non�inverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules. Keywords: Fuzzy time series, inverse fuzzy function, non-probabilistic model, non-inverse fuzzy model, future forecas

Modern technical solutions for controlling the modes of distribution networks with distributed energy sources

Relevance of the research. The world power industry has traditionally developed through the centralization in the creation of increasingly powerful energy equipment and its integration into energy complexes. As a result, large territorially long energy systems have been formed: European ENTSO-E, UES of Russia, UES of Ukraine and others. In recent years, there has been a stable tendency to change the general concept of energy development. We are talking about the introduction of a new ideology - energy of sustainable development. An important element of such an ideology is the significant use of distributed generation (DG) sources - low-power power plants. Introduction of DG sources in electrical networks (EN), in particular built on the basis of use renewable energy sources, in addition to reducing the environmental impact and solving many problems related to emissions and waste in production electricity, will, firstly, significantly increase the efficiency of the use of primary re- resources and - in the future - to reduce the cost of electricity, and secondly, to unload distribution network (DN), and finally, "push" the process of modernization of electrical objects and thus increase the reliability of electricity supply. This, in turn, will allow decrease the risks associated with further increases in energy prices. But there are number of technical issues related to the impact of DG sources on planning, organization of operation and control of electrical networks. The purpose of the research is to study issues related with the growing spread of use of renewable energy sources in distribution network and how to effectively solve them. To achieve this goal, the following tasks were set and solved: - the analysis of the current situation with distributed generation in the world; - investigating the impact of DGs on the operation of the distributed network. - the possibility of using effective electronic devices to solve problems related to DGs integration in electrical networks; - to study forecasting techniques for the efficient use of electronic devices and other communication equipment. The object of research. Processes of electricity distribution in electrical networks with local energy sources. The subject of research is the optimization electrical networks in the context of increasing use of renewable energy sources. Practical value of the results. To solve the set tasks in the dissertation the effectiveness and usefulness of the electronic SOP technology and one of the methods of load forecasting for the more effective operation of distribution networks were considered. Scientific novelty of the obtained results. For the first time, a comprehensive analysis of the feasibility of using complex of devices of power electronics for the purpose of increase of efficiency distribution networks under conditions of wide integration in them of various distributed sources of electric power generation, which will allow to evaluate the effectiveness of their use in power systems of Ukraine. The method of justification of Soft Open point technology application during the control modes of distribution networks with the distributed power generation sources has been further developed, which allows minimizing electric power losses. Improved the method of forecasting under conditions of uncertainty of the available input information in order to determine the expected network nodes loads and output power of the distributed power sources, which allows to increase effectiveness of application of remotely controlled switching centers application of remotely controlled switching devices distribution grids

Modeling Energy Demand—A Systematic Literature Review

In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.BMBF, 03SFK4T0, Verbundvorhaben ENavi: Energiewende-Navigationssystem zur Erfassung, Analyse und Simulation der systemischen Vernetzungen" - Teilvorhaben T0BMWi, 03ET4040C, Verbundvorhaben: Harmonisierung und Entwicklung von Verfahren zur regional und zeitlich aufgelösten Modellierung von Energienachfragen (DemandRegio) Teilvorhaben: ProfileDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Application of Fuzzy Time Series Approach in Electric Load Forecasting

In electrical power management, load forecasting accuracy is an indispensable factor which influences the decision making and planning of power companies in the future. Previous research has explored various forecasting models to resolve this issue, ranging from linear and non-linear regression to artificial intelligence algorithm. However, the absolute percentage error has yet to significantly improve using these models. Through this paper, the fuzzy time series (FTS) model was suggested to obtain better forecasted values and increases the forecasting accuracy. This accuracy could be obtained by using effective length of intervals of the discourse universe. The yearly dataset of Taiwan regional electric load was used for this empirical study and the reliability of the proposed model was compared with other previous models. The results indicated that the mean absolute percentage error (MAPE) of the proposed model (FTS) is smaller than MAPE obtained from those previous models