Analysis and comparison of various methods available for load forecasting: An overview

Khatoon, Shahida; Ibraheem,; Singh, Arunesh Kr.; Priti,

doi:10.1109/cipech.2014.7019112

Cited by 14 publications

(7 citation statements)

References 46 publications

(38 reference statements)

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“…Metode peramalan berdasarkan teknik penerapannya secara umum dibagi menjadi 2 (dua) pendekatan yaitu secara konvensional yang menggunakan perhitungan matematika serta menggunakan metode yang berdasarkan kecerdasan buatan [5]. Masing -masing metode ini memiliki kelebihan.…”

Section: Kataunclassified

Penentuan Target Pajak Kendaraan Bermotor Di Provinsi Bali Menggunakan ARIMA Dan Algoritma Genetik

Widhura¹,

Sudarma²,

Hartati³

2018

JTE

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Bali Regional Income Board is a regional organization tasked with determining the amount of local tax revenue target for the next fiscal year. Currently still done manually in accordance with existing upgrading trends from previous years. So it needs to be done in way that can be measured and accurate forecasting. In recent studies, it shows that forecasting by combining conventional and artificial intelligence (hybrid) methods results in better forecasting accuracy. By that reason, the writer tries to forecast the target of revenue from Motor Vehicle Tax (PKB and BBNKB), which contribute 70% to Bali Province income by combining ARIMA method and Genetic Algorithm. The data used consisted of five groups: yearly and new Vehicles that have linear data types, and as Reverse Names, Entrance Mutations and Output Mutations that have non-linear data types. Each data group consisted of PKB and BBNKB, where it’s monthly realization data from 2011 to 2016 used to be training data and realization data for 2017 as test data. The Combined forecasting mechanism is performed using ARIMA to forecast linear data and using Genetic Algorithms for non-linear data. As a benchmark for combined forecasting using ARIMA and Genetic Algorithms, forecasting using ARIMA and Genetic Algorithms independently is used for all data types (linear and nonlinear). Testing is done by comparing data of forecasting result with that 3 different methods for year 2017 with data realization year 2017. Then the error percentage is counted using MAPE. From the test results obtained for ARIMA MAPE value of 3.63, Genetic Algorithm 4.72 and combined ARIMA and Genetic Algorithm of 1.13. Thus, the result of forecasting with combination ARIMA and Genetic Algorithm have the best result and then used to forecasting target of PKB for 2018 and so on

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Section: Kataunclassified

Penentuan Target Pajak Kendaraan Bermotor Di Provinsi Bali Menggunakan ARIMA Dan Algoritma Genetik

Widhura¹,

Sudarma²,

Hartati³

2018

JTE

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“…Yük tahmini için kullanılan istatistiksel yöntemler yükün zaman, hava ve tüketici davranışlarının bir fonksiyonu olarak tanımlanmasını saglayan matematiksel model oluşturulmasını gerektirmektedir [12].…”

Section: öNceki̇ çAlişmalarunclassified

“…Yük tahmini üzerine yapılan çalışmalarda geçmiş yük verisine ilave olarak meteorolojik ölçümler ve fiyat verisinin kullanıldıgı görülmektedir [12]. Veri kümesi üzerinde yapılan çalışmalarda, mevcut günlük meteorolojik veriler ile saatlik yük arasında ilinti bulunmadıgı görülmüştür.…”

Section: A Giriş Parametrelerinin Seçilmesiunclassified

Power load forecast system for Turkish electric market

Bozkurt

Taysi

Biricik

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe -Elektrik piyasasında talep edilecek yükün önceden bilinmesi, buna baglı olarak üretim, satın alma, bakım ve yatırım gibi kararların alınması serbest piyasa koşullarında oldukça önemlidir. Fiyat tahminlerinin dogru yapılabilmesi de dogru yük tahminine baglıdır. Bu çalışmada, ileri beslemeli yapay sinir agları kullanılarak takvim, geçmiş fiyat ve yük verilerinin Türkiye Serbest Elektrik Piyasası için kısa dönemli yük tahminine olan etkileri sınanmıştır. Her bir verinin tahmin üzerindeki etkisi degerlendirilmiş ve takvim bilgisi, geçmiş fiyat bilgileri ve yük tahmini birleşimi kullanılarak yapılan tahmin ile en yüksek başarım elde edilmiştir.Anahtar Kelimeler-serbest elektrik piyasası, yük tahmini, yapay sinir agları Abstract-Forecasting the electric load demand in advance is very important in deregulated market conditions to give proper production, purchase, maintenance and investment decisions. Correct price forecasts also depend on accurate load prediction. In this study, effects of calendar, historical price and load data on short-term load forecast for Turkish Deregulated Electricity market are tested using feed forward neural networks. The impact of each data on forecast performance is evaluated and best performance is obtained using a combination of historical and predicted load, calendar information and historical price information Keywords-electric market, load prediction, artificial neural networks

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“…The concept of electricity consumption forecasting is first introduced by Samuel Insull in 1894 [1]. With the increase of distributed energy systems and the diffusion of demand management, the power system management faces more and more challenges.…”

Section: Introductionmentioning

confidence: 99%

A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling

Wei

Wen

Luo

2022

IET Generation Trans & Dist

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The short‐term electricity consumption forecasting can help to ensure the safe and reliable operation of the power system. Power companies usually need to report the electricity consumption of the current month five to seven days in advance and make a power generation plan for the next month. The existing studies are usually lack of appropriate feature selection methods and hard to achieve satisfactory results. This paper proposes a short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling. The maximum information coefficient (MIC) is employed to analyse the feature correlation, the electricity consumption curves are converted to several sub‐sequences of different frequency bands by the variational mode decomposition (VMD) to describe signal characteristics accurately, a hybrid model based on bidirectional gated recurrent unit (BiGRU) is innovated to extract the temporal and spatial features of the data and capture the contextual information from the complete time series, attention mechanism is used to do extract useful information and assign weights to make forecast. Compared with several benchmark methods, the proposed approach achieves better electricity consumption curve fitting and higher forecasting accuracy with the increase of forecasting step size.

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Analysis and comparison of various methods available for load forecasting: An overview

Cited by 14 publications

References 46 publications

Penentuan Target Pajak Kendaraan Bermotor Di Provinsi Bali Menggunakan ARIMA Dan Algoritma Genetik

Penentuan Target Pajak Kendaraan Bermotor Di Provinsi Bali Menggunakan ARIMA Dan Algoritma Genetik

Power load forecast system for Turkish electric market

A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling

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