Wind power forecast using neural networks: Tuning with optimization techniques and error analysis

Abstract

The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power forecast is imperative for systems operators, aiming at an efficient and economical wind power operation and integration into the power system. This work addresses the issue of forecasting short-term wind speed and wind power for 1 hour ahead, combining artificial neural networks (ANNs) with optimization techniques on real historical wind speed and wind power data. Levenberg-Marquardt (LM) and particle swarm optimization (PSO) are used as training algorithms to update the weights and bias of the ANN applied to wind speed predictions. The forecasting performance produced by the proposed models are compared with each other, as well as with the benchmark persistence model. Test results show higher performance for ANN-LM wind speed forecasting model, outperforming both ANN-PSO and persistence. The application of ANN-LM to wind power forecast revealed also a good performance, with an average improvement of 2.8% in relation to persistence. An innovative analysis of mean absolute percentage error (MAPE) behaviour in time and in typical days is finally offered in the paper. KEYWORDSartificial neural network, Levenberg-Marquardt, particle swarm optimization, short-term wind forecast | INTRODUCTIONWind power is the fastest growing source of renewable energy in the world. It represents a clean and sustainable source of energy and is in abundant supply, which helps to explain the growth in installed capacity of wind power plants in recent years. This implies the need to efficiently integrate the power generated from wind energy into existing power systems.However, the increase in wind power penetration requires a number of issues to be addressed. Since the wind power has a cubic relationship with wind speed, any error in the wind speed forecast leads to a larger error in wind power production. This dependency in the stochastic nature of wind speed also causes uncertainty in wind power production, and unexpected variations of wind power output may increase the operating costs for the overall power system. Thus, the use of accurate short-term wind power forecast techniques is crucial in the planning of economical dispatch, aiming to an efficient and economical wind power integration and operation. This will enable to mitigate the undesirable effects of wind fluctuations in the operation of power systems, namely, by reducing the spin reserve margin capacity and increase wind power penetration. /journal/we 810 Recently, with the development of artificial intelligence (AI), various AI methods for wind speed and wind power prediction have been developed and are being proposed, as for instance, artificial neural networks (ANNs), fuzzy logic and neuro-fuzzy, evolutionary algorithms (as particle swarm optimization [PSO]), and some hybrid methods. Also, wavelets and Markov chains are being used to capture the relevant pat...

A novel fractional discrete nonlinear grey Bernoulli model for forecasting the wind turbine capacity of China

“…Sideratos et al (2007) proposed a new approach to predict wind power by combining artificial intelligence techniques, and their new model provided an assessment of the quality of meteorological forecasts, which further improved forecasts. Nazaré et al (2020) pointed out that the ANN-LM algorithm was more suitable for forecasting short-term wind speed and wind power. Foley et al (2012) proposed a deeper review of methods and advances in recent years.…”

A state-of-the-art review on wind power deterministic prediction

“…The optimization algorithm is used to optimize the parameters of the standard wind power prediction model to improve the prediction accuracy. These models include particle swarm optimization algorithm optimized ANN (Nazare et al, 2019), particle swarm optimization algorithm optimized ELM (Zhou et al, 2018), ant colony optimization algorithm optimized ELM (Carrillo et al, 2018), Gaussian mixture model by Riemann limited-memory Broyden Fletcher Goldfarb Shanno (L-BFGS) optimization (Ge et al, 2018), and so on. How to choose the appropriate optimization algorithm and its parameters is an important problem.…”

Short‐term wind speed forecasting using S‐transform with compactly supported kernel

“…A nonlinear autoregressive neural network model with LM algorithm is applied on the decomposed subseries, for forecasting 37 …”