A Short-Term Wind Power Forecasting Tool for Vietnamese Wind Farms and Electricity Market

Viet, Dinh Thanh; Phuong, Vo Van; Duong, Minh Quan; Kies, Alexander; Schyska, Bruno U.; Wu, Yuan‐Kang

doi:10.1109/gtsd.2018.8595557

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…The results show that the forecasting models PSO-PSO-ANN and GA-PSO-ANN provided better results than the PSO-ANN or Adam-ANN models. Table 4 shows the comparison of MAPE between the different wind power forecasting models [8,27,47]. The proposed models PSO-PSO-ANN and GA-PSO-ANN showed better accuracy than the aforementioned models, which indicates that the proposed models in this model can be well used for forecasting in the actual production of wind farms while ensuring a higher level of accuracy.…”

Section: Discussionmentioning

confidence: 95%

“…These data were collected periodically with a 30-min cycle, taken from Tuy Phong wind farm, located in Binh Thuan Province, Vietnam. The entire dataset consists of 3866 records [8]. The 3866 data records were divided into two sets (i.e., training set and test set).…”

Section: Datamentioning

confidence: 99%

“…Another model was developed using ANN with Gaussian process approximation and adaptive Bayesian learning to predict wind power generation capacity in a short time with intervals such as 5 min, 10 min, 15 min, 20 min, and 30 min [7]. Other researchers have built models utilizing neural networks to predict the wind power generation capacity that can be applied to the power system and electricity market [8]. Various forecasting methods were combined including persistent methods, backpropagation neural networks, and radial basis function (RBF) neural networks to build models for 10-min-ahead wind power prediction [9].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the neural network structure includes one input layer, one hidden layer, and one output layer, which can be seen in Figure 3 [8]. The weight connected from the ith neuron in the input layer to the hth neuron in the hidden layer is denoted by w 1 ih , and the weight connected from the hth neuron in the hidden layer to the kth neuron in the output layer is denoted by w 2 h .…”

mentioning

confidence: 99%

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Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

et al. 2020

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As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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Section: Discussionmentioning

confidence: 95%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

et al. 2020

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“…Additionally, in some locations, it is less expensive to develop new wind and solar PV power facilities than to keep operating the ones that are already in operation. PV panel deployment has expanded recently in order to produce electricity from solar energy because it is a clean, accessible, cost-free, and renewable source of energy [2]. PV panel installation is probably going to keep rising due to governments' interest in investing in renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Solar Prediction in Multi-Area Networks

Sumit Saroha

2023

tjjpt

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A clean, readily accessible and renewable energy source is solar energy. The use of photovoltaic panels is a popular trend to harness solar energy for the production of electrical power. Variable generated electricity is a result of solar energy's intermittent nature. An energy management system with a solar forecast module is presented in this study. The main purpose of this study is to apply ANN for verifying and predicting solar energy in India. The output is controlled by an ANN controller. To meet the demands put forward by the Energy Management System (EMS), artificial neural networks (ANNs), a subset of statistical approaches, were selected as the prediction method. Sun rising and sun set times are included in the total of 10 input factors taken into account for solar prediction data. For the EMS to operate properly, there are needs that include accurate forecasting in the short-term prediction horizon. The Mean Square Error (MSE) parameter is used to measure performance, and all findings are implemented in the Python language. This study shows the low RMSE value of that particular area and ANN is an efficient method for this study.

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A review of short-term wind power generation forecasting methods in recent technological trends

Arslan Tuncar,

Sağlam,

Oral

2024

Energy Reports

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A Short-Term Wind Power Forecasting Tool for Vietnamese Wind Farms and Electricity Market

Cited by 9 publications

References 7 publications

Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Artificial Neural Network Based Solar Prediction in Multi-Area Networks

A review of short-term wind power generation forecasting methods in recent technological trends

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