Very short-term load forecasting based on NARX recurrent neural networks

Andrade, Luciano Carli Moreira de; Oleskovicz, Mário; Santos, Athila Quaresma; Coury, Denis Vinicius; Fernandes, Ricardo Augusto Souza

doi:10.1109/pesgm.2014.6939012

Cited by 13 publications

(12 citation statements)

References 7 publications

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“…and given the research that shows that accurate predictions can be made with single methods [8,25,26], hybridisation was discarded.…”

Section: Of 19mentioning

confidence: 99%

“…In particular, being able to obtain accurate energy demand forecasts is one of the key challenges that researchers are trying to overcome by using different techniques [4][5][6][7] to develop better control strategies. Nevertheless, predicting energy demand in microgrids is usually more complex than in conventional grids owing to the fact that the load time curves of a microgrid are much more volatile than those of a traditional power system [8]. Although the forecaster proposed in this paper has been developed for microgrids, it can also be used for bigger power systems due to their lower volatility.…”

Section: Introductionmentioning

confidence: 99%

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Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control

et al. 2020

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Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites.

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“…and given the research that shows that accurate predictions can be made with single methods [8,25,26], hybridisation was discarded.…”

Section: Of 19mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control

et al. 2020

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“…The study by De Andrade [41] presents an implementation of a dynamic recurrent NARX ANN for load forecasting. The application is an electric substation, and the prediction forecast is for very short-term load forecasting (5 min) in order to feed an automatic generation control (AGC) in order to maintain the balance between the demand and supply of electricity.…”

Section: Narx Ann Network and Arx Knowledge-based Modelsmentioning

confidence: 99%

Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

Buitrago

Asfour

2017

Energies

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Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

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“…The application of the NARX-NN on STLF is tested in [13]- [15], in which the authors forecast megawatt (MW) level loads in large scales. For small scales, however, the NARX-NN is not widely tested.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Load Forecasting of Active and Reactive Powers in Small Scales using LASSO-Integrated Nonlinear Autoregressive Neural Networks

Zhang¹

2016

Preprint

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Abstract-Short-term load forecasting (STLF) is important for power system planning and optimization, especially in the dynamic environment of smart grid. Traditional load forecasting is implemented at substation levels to predict the upcoming active power and optimal system settings. In more advanced smart grid applications, e.g. the Volt-VAR Control, small-scale load forecasting opens up new opportunities in coordinating distributed resources such as distributed generation (DG) with utilities' efficiency missions. This paper proposes a STLF approach for small residential blocks with 10-12 households. The Nonlinear Autoregressive Neural Network (NAR-NN) is employed to predict hour-ahead active (P) and reactive (Q) powers with a moving window of training data. The regressor shrinkage technique, LASSO, is used to improve the selection of the regressors in the NAR-NN model by removing insignificant input features. The results show the forecasting performance could be enhanced by 20% comparing to feed-forward Artificial Neural Networks (ANNs). The improvement in forecasting both P & Q could accommodate new smart grid applications in small scales.Index Terms-Short-term load forecasting, nonlinear autoregressive neural network, regression shrinkage, power system planning, smart grid.

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Very short-term load forecasting based on NARX recurrent neural networks

Cited by 13 publications

References 7 publications

Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control

Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control

Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

Short-Term Load Forecasting of Active and Reactive Powers in Small Scales using LASSO-Integrated Nonlinear Autoregressive Neural Networks

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