Forecasting of Wind Speed in Malang City of Indonesia using Adaptive Neuro-Fuzzy Inference System and Autoregressive Integrated Moving Average Methods

Suyono, Hadi; Prabawanti, Dinda Oki; Shidiq, Mahfudz; Hasanah, Rini Nur; Wibawa, Unggul; Hasibuan, Arnawan

doi:10.1109/ict-pep50916.2020.9249867

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…An estimate of the increase in the number of electricity customers every year is an initial step that must be done in formulating an electricity system planning policy. Estimates made based on facts and data, estimates that are too high will result in losses for the company and conversely estimates that are too low will cause losses for customers [4]. This has an impact on the disruption of the flow of electricity in an area.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Electricity Need for the Household Sector PT. PLN (Persero) Rayon Lhoksukon with Time Series Linear Regression

Habibah

Muhammad

Randi

2022

AJEEET

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Predictions of electricity demand such as connected power and the country's consumption of electrical energy continue to increase every year by population growth in Indonesia. This will cause the number of electricity customers to increase. PLN as a provider of electricity is important to predict long-term electricity demand as the direction of the company's economic policy to meet customer needs. This activity will be reviewed in the electricity distribution area of PT. PLN (PERSERO) Rayon Lhoksukon and its surroundings. Prediction of electricity demand is done through forecasting the number of electricity customers using the time series analysis method with regression from the previous data review from 2016 to 2019. The results show, the form of forecasting , results in the number of electricity customers in 2030 being 6005 customers or an average increase of 2 0.08%/year. The functional relationship between the number of subscribers and connected power is close to a straight line, approached by linear regression, in the form of , resulting in connected power in 2030 being 6927851.4 VA or an average increase of 2.51%/year . Meanwhile , the functional relationship between the number of customers and consumption of electrical energy is close to a straight line, approached by linear regression, in the form of , resulting in electrical energy consumption in 2030 being 67580665.04 kWh or an average increase of 1.15%/year

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Section: Introductionmentioning

confidence: 99%

Forecasting the Electricity Need for the Household Sector PT. PLN (Persero) Rayon Lhoksukon with Time Series Linear Regression

Habibah

Muhammad

Randi

2022

AJEEET

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“…Researchers have proposed a variety of methods for wind speed forecasting, including statistical methods, physical method, ANN [2,3], and support vector machines (SVM) [4]. Statistical forecasting method is a method based on actual historical data, theoretical knowledge, and mathematical model to make quantitative forecasting about the development of things, mainly including the trend extrapolation method, regression forecasting method [5], Delphi method [6], subjective probability method, exponential smoothing (ES) method [7,8], autoregressive integrated moving average (ARIMA) [9], fuzzy system (FS) [10] and other methods. Singh et al [11] proposed a new Repeated wavelet transform (WT) based ARIMA (RWT-ARIMA) model, which has improved accuracy for very short-term wind speed forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Combined Model Based on Society Cognitive Optimization Algorithm for Wind Speed Forecasting

Chen

2021

Preprint

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Wind energy, as renewable energy, has drawn the attention of society. The use of wind power generation can reduce the pollution to the environment and solve the problem of power shortage in offshore islands, grassland, pastoral areas, mountain areas, and highlands. Wind speed forecasting plays a significant role in wind farms. It can improve economic and social benefits and make an operation schedule for wind turbines in large wind farms. At present, researchers have proposed a variety of methods for wind speed forecasting; artificial neural network (ANN) is one of the most commonly used methods. This paper proposes a combined model based on the existing artificial neural network algorithms for wind speed forecasting at different heights. We first use the wavelet threshold method to the original wind speed data set for noise reduction. After that, the three artificial neural networks, extreme learning machine (ELM), Elman neural network, and Long Short-Term Memory neural network (LSTM), are applied for wind speed forecasting. In addition, variance reciprocal method and society cognitive optimization algorithm (SCO) are used to optimize the weight coefficients of the combined model. In order to evaluate the forecasting performance of the combined model, we select wind speed data at three heights (20m, 50m, and 80m) in National Wind Technology Center M2 Tower. The experimental results show that the forecasting performance of the combined model is better than the single model, and it has a good forecasting performance for the wind speed at different heights.

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“…Автори статей використовують їх для прогнозування Вісник Національного технічного університету «ХПІ». Серія: Енергетика надійність та енергоефективність, № 1 (1) 2020 79генерування ВЕС[15][16][17] та ФЕС[18,19] ст = м • /100(1)де Еефективність сонячних модулів (15,9 %); Sмсумарна площа встановлених панелейм = • м0 ,(2)де nкількість встановлених панелей (наприклад, для ФЕС «Цекинівська-2» 4-5 черга n = 4356); Sм0площа однієї фотоелектричної панелі (для ФЕС «Цекинівська-2» 4-5 черга Sм0 = 1,6635).…”

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Determination of Ress Generation Using Artificial Neural Networks

Rubanenko¹,

Danylchenko²,

Teptya³

2020

ЕНЕЕ

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Paper considers the perspectives and potential of using renewable energy sources to decide the global warming problem. The World trend of increasing electricity generation by photovoltaic power stations according to the International Renewable Energy Agency and the trend of increasing the installed capacity of photovoltaic power stations in Ukraine, which supply the generated capacity at a "green" tariff according to the National Commission for State Regulation of Energy utilities of Ukraine. Opportunities and conditions of using artificial neural networks to defined the power generation of photovoltaic power stations on the example of the power plant "Tsekinivska-2" 4–5 turns are investigated. A platform developed by the European Commission – Photovoltaic Geographical Information System – was used to create a database for the creation and training of artificial neural networks. Regularities of change of meteorological satellite data and their influence on electricity generation of photovoltaic power stations are established. For this purpose, the software complex MATLAB was used, namely the module for the creation of artificial neural networks – Neural Networks Toolbox. The height of the sun is conditionally considered constant and its value is repeated from year to year or has a slight deviation, so it can be used as an indicator of the hour and can be considered known in advance, so determined by empirical formulas and changes only under certain astrophysical laws. Regarding the temperature at 2 m and the wind at 10 m, these meteorological data are known, as they are needed not only for forecasting the operation of renewable energy sources but also in agriculture. Therefore, data related to solar radiation are considered to be the most problematic, as this value is the most difficult to determine. Satellite data may have an error, the installation of weather stations, namely quality pyranometers is a costly procedure, but will help provide a training sample of quality data. To forecast with satisfactory accuracy, it is necessary to collect data for 1 year of operation of the weather station. The nntool and Anfis MATLAB modules were used to predict generation. But the obtained results can be used to assess the effectiveness of the photovoltaic power stations, but they are unsatisfactory for the operational balancing of the system.

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Forecasting of Wind Speed in Malang City of Indonesia using Adaptive Neuro-Fuzzy Inference System and Autoregressive Integrated Moving Average Methods

Cited by 6 publications

References 11 publications

Forecasting the Electricity Need for the Household Sector PT. PLN (Persero) Rayon Lhoksukon with Time Series Linear Regression

Forecasting the Electricity Need for the Household Sector PT. PLN (Persero) Rayon Lhoksukon with Time Series Linear Regression

A Combined Model Based on Society Cognitive Optimization Algorithm for Wind Speed Forecasting

Determination of Ress Generation Using Artificial Neural Networks

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