A sequential approach for short-term water level prediction using nonlinear autoregressive neural networks

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

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Short-Term Water Demand Forecasting Using Nonlinear Autoregressive Artificial Neural Networks

Journal of Water Resources Planning and Management ◽

10.1061/(asce)wr.1943-5452.0001165 ◽

2020 ◽

Vol 146 (3) ◽

pp. 04020008 ◽

Cited By ~ 6

Author(s):

Mo’tamad H. Bata ◽

Rupp Carriveau ◽

David S.-K. Ting

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Water Demand ◽

Demand Forecasting ◽

Short Term ◽

Water Demand Forecasting ◽

Artificial Neural ◽

Nonlinear Autoregressive

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Real-Time Water Level Prediction Based on Artificial Neural Networks

New Trends in Urban Drainage Modelling - Green Energy and Technology ◽

10.1007/978-3-319-99867-1_104 ◽

2018 ◽

pp. 603-607

Author(s):

Berkhahn Simon ◽

Neuweiler Insa ◽

Fuchs Lothar

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Water Level ◽

Real Time ◽

Artificial Neural ◽

Water Level Prediction

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Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

Abstract and Applied Analysis ◽

10.1155/2014/984268 ◽

2014 ◽

Vol 2014 ◽

pp. 1-21

Author(s):

Xuejun Chen ◽

Jing Zhao ◽

Wenchao Hu ◽

Yufeng Yang

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Weighted Average ◽

Wind Farms ◽

Ensemble Empirical Mode Decomposition ◽

Short Term ◽

Wind Speed Forecasting ◽

Hybrid Approaches ◽

Mode Decomposition ◽

Nonlinear Autoregressive

As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.

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

10.31224/osf.io/v2gj9 ◽

2016 ◽

Cited By ~ 1

Author(s):

Guanchen Zhang

Keyword(s):

Neural Networks ◽

Smart Grid ◽

Load Forecasting ◽

Small Scale ◽

Short Term ◽

Distributed Resources ◽

Short Term Load Forecasting ◽

Small Scales ◽

Grid Applications ◽

Nonlinear Autoregressive

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.

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