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 9 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The flowchart of the short-term electricity load forecasting (Suyono et al, 2020) process using MATLAB Graphical User Interface (GUI) in this research can be seen in Fig. 8.…”

Section: Methodsmentioning

confidence: 99%

Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic

Rizaldi,

Ridwan,

Anisa

et al. 2023

J. Renew Energ. Electric. Comp Eng

View full text Add to dashboard Cite

The high demand for electricity in the production process at PT Semen Padang requires a system that can cope with various kinds of disturbances. The problem is that the need for electrical loads is dynamic, especially in the short term, allowing fluctuations between electrical loads at uncertain times. A short-term electric energy consumption forecasting method is needed to determine load growth and distributed power supply. This research aims to use a fuzzy logic algorithm to perform short-term electrical energy consumption forecasting and compare the forecasting results with the actual load at PT Semen Padang. The results showed that short-term load forecasting for seven days using the fuzzy Mamdani method, namely Smallest of Maximum (SoM), obtained a percentage MAPE value of 8.15%. Meanwhile, the Weight of Average (WoA) Sugeno defuzzification method gets a portion of the MAPE value of 9.51%. The Sugeno method is more accurate than the Mamdani method in short-term electricity load forecasting for PPI Indarung V PT Semen Padang. If based on the time category, then forecasting the electricity load on holidays is better than predicting on weekdays. However, when viewed in terms of per day, in Wednesday has the smallest average MAPE value of 5.05%.

show abstract

“…The flowchart of the short-term electricity load forecasting (Suyono et al, 2020) process using MATLAB Graphical User Interface (GUI) in this research can be seen in Fig. 8.…”

Section: Methodsmentioning

confidence: 99%

Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic

Rizaldi,

Ridwan,

Anisa

et al. 2023

J. Renew Energ. Electric. Comp Eng

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 9 publications

References 11 publications

Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic

Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic

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

Contact Info

Product

Resources

About