scholarly journals METODE REGRESI LINIER UNTUK PREDIKSI KEBUTUHAN ENERGI LISTRIK JANGKA PANJANG (STUDI KASUS PROVINSI LAMPUNG)

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
M. Syafruddin ◽  
Lukmanul Hakim ◽  
Dikpride Despa
Keyword(s):  
Logical Consequence ◽  
Electricity Consumption ◽  
Load Factor ◽  
Electrical Load ◽  
Property Development ◽  
System Planning ◽  
Population Number ◽  
Electrical Load Forecasting ◽  
Consumption Prediction ◽  
Comprehensive Study

Abstrak— Meningkatnya pembangunan di Provinsi Lampung terutama di sektor perumahanbaik sederhana maupun rumah mewah yang membawa konsekuensi logis berupapeningkatan kebutuhan tenaga listrik. Sebuah studi komprehensif dalam rangkapenyedian tenaga listrik di Lampung menjadi kebutuhan yang mendesakdilakukan untuk membuat rencana operasi sistem  tenaga listrik. Salah satu faktoryang sangat menentukan dalam membuat rencana operasi sistem tenaga listriktersebut adalah prediksi beban listrik yang akan ditanggung oleh sistem tenagalistik yang bersangkutan. Penelitian ini bertujuan untuk memprediksi  kebutuhan energi  listrik  di  Provinsi  Lampung  hingga  Tahun 2030, diharapkan dapatdijadikan sebagai masukkan dalam melakukan perencanaan pembangunan sistemtenaga listrik. Prediksi kebutuhan energi listrik Lampung dibagi menjadi 4 sektoryaitu : sektor rumah tangga, bisnis, publik, dan industri. Proses perancanganprediksi kebutuhan energi listrik menggunakan 6 variabel dan dibagi menjadi 2 parameter,  yaitu: parameter ekonomi (produk domestik regional bruto, jumlahpenduduk, jumlah rumah tangga) dan parameter listrik (rasio elektrifikasi,  faktor beban,  losses). Dengan menggunakan metode regresi linier untuk memprediksivariabel-variabel di atas, diperoleh hasil prediksi daya listrik tersambung totalpada tahun 2028 sebesar 2.841,78 MVA (rata-rata pertumbuhannya sebesar 2,38 %), dan konsumsi energi listrik pada tahun 2023 sebesar 5.934,98 Gwh (rata-rata pertumbuhannya sebesar 3, 83 %).  Kata Kunci —regresi linier, konsumsi energi listrik, Provinsi Lampung. Abstract The Increasing of property development in  Lampung Province, especially in thehousing sector both simple and luxurious brings a logical consequence ofelectricity  demand.  A  comprehensive  study  for  electricity  provisioning  inLampung become requirement constrain. Electrical load forecasting is  one of theimportant factors is power system planning and making. Prediction of electricityconsumption there are for activity that need to  be predicted i.e.: households,businesses, public services, and industry. 6 variables include are considered grossregional domestic product, population, number of households, electrification ratio,load factor, losses are considered to be influencing the forecasting proses. Linearregression method was used to predict all variables. The result of total electricitypower connected prediction on 2028 is 2841.78 MVA (growth average at of 2,38%). And electricity consumption prediction on 2023 is 5934.98 Gwh (growthaverage at 3, 83%).  Keywords—linier regression, electricity consumption, Lampung Province.

Forecasting Methods in Electric Power Sector

2018 ◽  
Vol 7 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Sujit Kumar Panda ◽  
Alok Kumar Jagadev ◽  
Sachi Nandan Mohanty
Keyword(s):  
Electric Power ◽  
Financial Analysis ◽  
Management Strategies ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Load Management ◽  
Electrical Load ◽  
Electric Power Sector ◽  
Electrical Load Forecasting

Electric power plays a vibrant role in economic growth and development of a region. There is a strong co-relation between the human development index and per capita electricity consumption. Providing adequate energy of desired quality in various forms in a sustainable manner and at a competitive price is one of the biggest challenges. To meet the fast-growing electric power demand, on a sustained basis, meticulous power system planning is required. This planning needs electrical load forecasting as it provides the primary inputs and enables financial analysis. Accurate electric load forecasts are helpful in formulating load management strategies in view of different emerging economic scenarios, which can be dovetailed with the development plan of the region. The objective of this article is to understand various long term electrical load forecasting techniques, to assess its applicability; and usefulness for long term electrical load forecasting for an isolated remote region, under different growth scenarios considering demand side management, price and income effect.

A novel fractional discrete grey model with an adaptive structure and its application in electricity consumption prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yitong Liu ◽  
Yang Yang ◽  
Dingyu Xue ◽  
Feng Pan
Keyword(s):  
Fractional Order ◽  
Electricity Consumption ◽  
Grey Model ◽  
The Novel ◽  
Raw Data ◽  
Content Type ◽  
Power Load ◽  
Grey Models ◽  
Novel Model ◽  
Consumption Prediction

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

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