Wind Energy Forecasting Using Recurrent Neural Networks

Shabbir, Noman; Kütt, Lauri; Jawad, Muhammad; Amadiahanger, Roya; Iqbal, Muhammad Naveed; Rosin, Argo

doi:10.1109/bdkcse48644.2019.9010593

Cited by 14 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simple RNN structure consists of neural network loops with feedback. The RNN can connect the past information with the present task but there exists a vanishing and exploding gradient problem [30]. During the training, weights of the RNN are updated proportionally to the gradient of error ( ) with respect to weights ( ).…”

Section: B Recurrent Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination

Iqbal¹,

Jawad²,

Jaffery³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

The growing use of nonlinear devices is introducing harmonics in the power system networks that results in distortion of current and voltage signals causing damage to the power distribution system. Therefore, in power systems, the elimination of harmonics is of great concern. This paper presents an efficient techno-economical approach to suppress harmonics and improve the power factor in the power distribution network using neural network algorithms-based Shunt Hybrid Active Power Filter (SHAPF), such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN). The objective of the proposed algorithms for SHAPF is to reduce Total Harmonic Distortion (THD) within an acceptable range to improve system quality. In our filter design approach, we tested and compared conventional pq0 theory and neural networks to detect the harmonics present in the power system. Moreover, for the regulation of the DC supply to the inverter of the SHAPF, the conventional PI controller and neural networks-based controllers are used and compared. The applicability of the proposed filter is tested for three different nonlinear load cases. The simulation results show that the neural networks-based filter control techniques satisfy all international standards with minimum current THD, neutral wire current elimination, and small DC voltage fluctuations for voltage regulation current. Furthermore, all three neural network architectures are tested and compared based on accuracy and computational complexity, with RNN outperforming the rest.

show abstract

Section: B Recurrent Neural Networkmentioning

confidence: 99%

“…This research used LSTM based RNN control models for the regulation of DC voltage and for the detection of harmonics in the SHAPF [30]. In both of the aforementioned controls, LSTM based RNNs are trained using Adam as a training algorithm with 250 epochs and 200 hidden units to make the system more robust and adaptive.…”

Section: B Recurrent Neural Networkmentioning

confidence: 99%

Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination

Iqbal¹,

Jawad²,

Jaffery³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine Learning (ML) models have been introduced later on to reduce the time complexity of SVD in the STLF problem. ML algorithms have enhanced the performance of STLF by showing profound accuracy in dealing with non-linearities of the electrical load data and accurate forecasting of the peaks of electrical load as compared to the statistical regression and dimension reduction models [23][24][25]. ML methods mainly comprise Artificial Neural Networks (ANNs) which can handle the stochastic nature of the weather-sensitive loads during the prediction of electrical load forecasting [26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

Self Cite

View full text Add to dashboard Cite

Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

show abstract

“…U denotes the weight between the input and the hidden layers, V denotes the weight between the hidden and output layers, and W denotes the weight between the current hidden layer and the hidden layer in the future. Weight w will take the previously suggested state values from the hidden layer and subtract one, which is really the input whenever the state x t is altered and placed in the next hidden layer s t , or when the hidden layer is updated [10]. The following formulas regulate the calculation that performs in an RNN:…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…The uncertainty of forecasting the output of wind energy will affect the performance of the UC operation and may cause serious risks to the operation and control of the power system. In this research, a well-known artificial-intelligence-based approach called recurrent neural network (RNN) [10] is being used to forecast the day-ahead performance of the wind energy prelude to be used as the generating unit in the IEEE 30 test bus system, so as to plan the performance of the network operating system by using UC optimization approaches. Moreover, the uncertainty of the RNN method is being analyzed by applying the actual and forecasted wind power in the given network and measuring the performance of the operation of UC in both cases.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting

Salman

Kuşaf

2021

Sustainability

View full text Add to dashboard Cite

Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks.

show abstract

Wind Energy Forecasting Using Recurrent Neural Networks

Cited by 14 publications

References 9 publications

Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination

Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting

Contact Info

Product

Resources

About