Short-term wind speed forecasting of Oak Park Weather Station by using different ANN algorithms

With deregulation much attention is given to electricity price forecasting. Some of the factors which affect forecast are day of the week, weather, season, hour of the day, year, and special days. In general, the main factor which drives the electricity price is power demand. Here, ANN algorithms i.e. Bayesian Regularization, Levenberg Marquardt back propagation & Scaled Conjugate Gradient algorithms has been applied in short-term price forecasting that is, the one hour-ahead forecast of the electricity price using MATLAB R14a. The simulation results have shown accurate one hour ahead forecasts.

Although clean and sustainable wind energy has long been recognized as one of the most attractive electric power sources, generation of wind power is still much easier than its integration into liberalized electricity markets. One of the key obstacles on the way of wider implementation of wind energy is its highly volatile and intermittent nature. This has boosted an interest in developing a fully probabilistic forecast of wind speed, aiming to assess a variety of related uncertainties. Nonetheless, most of the available methodology for constructing a future predictive density for wind speed are based on parametric distributional assumptions on the observed wind data, and such conditions are often too restrictive and infeasible in practice. In this paper we propose a new nonparametric data-driven approach to probabilistic wind speed forecasting, adaptively combining sieve bootstrap and regime switching models. Our new bootstrapped regime switching (BRS) model delivers highly competitive, sharp and calibrated ensembles of wind speed forecasts, governed by various states of wind direction, and imposes minimal requirements on the observed wind data. The proposed methodology is illustrated by developing probabilistic wind speed forecasts for a site in the Washington State, USA.

Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization

ABSTRACTWind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.

Short-term wind speed forecasting based on the Jaya-SVM model

ARMA based approaches for forecasting the tuple of wind speed and direction

A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism

Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

Wind energy is one of the fastest progressing renewable energy sources. Wind speed forecasting has gained more attention as wind energy never causes air pollution and other hazards to the environment. Eco-friendliness and cost-effectiveness are the main reasons for the high demand for wind energy. Accurate and reliable prediction model for wind speed forecasting is very challenging because of its intermittent nature. Deep learning based architectures like Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) are considered as better models for time series prediction. LSTM neural network models are more appropriate for learning long term dependencies. In this paper, an LSTM network for a multi-step-ahead short term wind speed forecasting is proposed. As lag values have importance in time series prediction problems, tests are conducted to find optimal lag value. The value which contributes minimum mean square error is selected as optimal lag value. The study of the results reveals that the proposed LSTM model is more effective and efficient with high predictive accuracy compared to Auto-Regressive Integrated Moving Average (ARIMA) model.

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 h ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model and persistence, autoregressive, and autoregressive moving-average models. The ensemble is calibrated against observations for a 6-month period (January–June 2006) at a potential wind-farm site in Illinois using the Bayesian model averaging technique. The forecasting system is evaluated against observations for the July 2006–December 2007 period at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble as well the time series models under all environmental stability conditions. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 min. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network

This paper presents a novel neural network-based approach to short-term, multi-step-ahead wind speed forecasting. The methodology combines predictions from a set of feed forward neural networks whose inputs comprehend a set of 11 explanatory variables related to past averages of wind speed, direction, temperature and time of the day, and their outputs represent estimates of specific wind speed averages. Forecast horizons range from 30 minutes up to 6:30 hours ahead with 30 minutes time steps. Final forecasts at specific horizons are combinations of corresponding neural network predictions. Data used in the experiments are telemetric measurements of weather variables from five wind farms in eastern Canada, covering the period from November 2011 to April 2013. Results show that the methodology is effective and outperforms established reference models particularly at longer horizons. The method performed consistently across sites leading up to more than 60% improvement over persistence and 50 % over a more realistic MA-based reference.

Evaluation of hybrid forecasting approaches for wind speed and power generation time series

Very short-term wind speed forecasting with Bayesian structural break model

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It’s very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting.