Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador

López, Germánico; Arboleya, Pablo

doi:10.1016/j.renene.2021.10.070

Cited by 68 publications

(18 citation statements)

References 40 publications

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“…The improved, with the slight modifications proposed above, deep learning forecasting models presented in this paper were shown to perform better than conventional deep learning and autoregressive methods [69][70][71][72][73]. Moreover, they can also be applied to photovoltaic panel-and wind turbine-generated electric power forecasting.…”

Section: Discussionmentioning

confidence: 83%

“…In Tables 8 and 9, respectively, the average daily performance metrics for the two wellproven conventional methods examined (RegARMA and NARX) and the deep learning technique with the more accurate forecasting performance for solar irradiation (i.e., encoderdecoder LSTM) and windspeed (i.e., CNN1) are presented [72][73][74][75][76][77]. NARX is a nonlinear autoregressive exogenous model that has become popular in the last few years for its performance in timeseries forecasting problems, and RegARMA is a model that is based on regression with autoregressive-moving average (ARMA) timeseries errors.…”

Section: Evaluation Of Conventional Forecasting Performance Methods U...mentioning

confidence: 99%

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One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

2022

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In recent years, demand for electric energy has steadily increased; therefore, the integration of renewable energy sources (RES) at a large scale into power systems is a major concern. Wind and solar energy are among the most widely used alternative sources of energy. However, there is intense variability both in solar irradiation and even more in windspeed, which causes solar and wind power generation to fluctuate highly. As a result, the penetration of RES technologies into electricity networks is a difficult task. Therefore, more accurate solar irradiation and windspeed one-day-ahead forecasting is crucial for safe and reliable operation of electrical systems, the management of RES power plants, and the supply of high-quality electric power at the lowest possible cost. Clouds’ influence on solar irradiation forecasting, data categorization per month for successive years due to the similarity of patterns of solar irradiation per month during the year, and relative seasonal similarity of windspeed patterns have not been taken into consideration in previous work. In this study, three deep learning techniques, i.e., multi-head CNN, multi-channel CNN, and encoder–decoder LSTM, were adopted for medium-term windspeed and solar irradiance forecasting based on a real-time measurement dataset and were compared with two well-known conventional methods, i.e., RegARMA and NARX. Utilization of a walk-forward validation forecast strategy was combined, firstly with a recursive multistep forecast strategy and secondly with a multiple-output forecast strategy, using a specific cloud index introduced for the first time. Moreover, the similarity of patterns of solar irradiation per month during the year and the relative seasonal similarity of windspeed patterns in a timeseries measurements dataset for several successive years demonstrates that they contribute to very high one-day-ahead windspeed and solar irradiation forecasting performance.

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Section: Discussionmentioning

confidence: 83%

Section: Evaluation Of Conventional Forecasting Performance Methods U...mentioning

confidence: 99%

One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

2022

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“…4 A). The Muchinga escarpment has an average terrain elevation of about 932 m above sea level and greater than 1800 m at some points, therefore, higher wind speeds at this elevation can be attributed to the reduced effect of gravity and friction (López and Arboleya 2022). Other high elevations such as Mbala in the Northern Province of the country are also observed to experience generally high wind speeds (Fig.…”

Section: Spatial Wind Speed Variationsmentioning

confidence: 95%

Performance assessment of CORDEX regional climate models in wind speed simulations over Zambia

Libanda¹

2022

Model. Earth Syst. Environ.

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There is no single solution to cutting emissions, however, renewable energy projects that are backed by rigorous ex-ante assessments play an important role in these efforts. An inspection of literature reveals critical knowledge gaps in the understanding of future wind speed variability across Zambia, thus leading to major uncertainties in the understanding of renewable wind energy potential over the country. Several model performance metrics, both statistical and graphical were used in this study to examine the performance of CORDEX Africa Regional Climate Models (RCMs) in simulating wind speed across Zambia. Results indicate that wind speed is increasing at the rate of 0.006 m s− 1 per year. RCA4-GFDL-ESM2M, RCA4-HadGEM2-ES, RCA4-IPSL-CM5A-MR, and RCA4-CSIRO-MK3.6.0 were found to correctly simulate wind speed increase with varying magnitudes on the Sen’s estimator of slope. All the models sufficiently reproduce the annual cycle of wind speed with a steady increase being observed from April reaching its peak around August/September and beginning to drop in October. Apart from RegCM4-MPI-ESM and RegCM4-HadGEM2, the performance of RCMs in simulating spatial wind speed patterns is generally good although they overestimate it by ~ 1 m s− 1 in the western and southern provinces of the country. Model performance metrics indicate that with a correlation coefficient of 0.5, a root mean square error of 0.4 m s− 1, an RSR value of 7.7 and a bias of 19.9%, RCA4-GFDL-ESM2M outperforms all other models followed by RCA4-HadGEM2, and RCA4-CM5A-MR respectively. These results, therefore, suggest that studies that use an ensemble of RCA4-GFDL-ESM2M, RCA4-HadGEM2, and RCA4-CM5A-MR would yield useful results for informing future renewable wind energy potential in Zambia.

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“…Yu et al 33 proposed an improved Long Short-Term Memory-enhanced forget-gate network model, abbreviated as LSTM-EFG, used to forecast wind power. Lo´pez and Arboleya 34 proposed a developed approach with the application of linear regression models as the baseline, and RNN, LSTM network, and Dynamic Neural Networks (DNN), Nonlinear Autoregressive Exogenous (NARX) network to perform accurate wind speed forecasting in complex terrain in the Ecuadorian Andes to identify feasible places for wind energy applications. Srivastava and Tripathi 35 predicted the power generated from wind energy using wind velocity via wind turbine, using RNN, LSTM, and GBM, to find out which is the better one based on the performance parameters values.…”

Section: Literature Reviewmentioning

confidence: 99%

LSTM neural network techniques-based analytical predictive models for wind energy and mechanical power

Masri

Al-Jabi

2022

Advances in Mechanical Engineering

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Nowadays, the importance of renewable energy is rapidly increasing. It is considered as an alternative clean source of energy due to environmental reasons. Therefore, this research presents a data analysis model to predict the generated electrical power based on wind energy and the long short-term memory (LSTM) model. The work focused on the Spring and Autumn seasons where wind speed has high variation and the data was collected every 15 min in a wide, open space area located in southeast Palestine. To investigate and validate the correctness and robustness of the work, three different scenarios were performed for each season to predict wind speed and direction, and mechanical power. Also, different performance metrics were applied. The results were very promising with an average error of less than 3% and an R-Squared value of 0.95. Since the price of electricity in Palestine is relatively high, the results showed also the possibility to generate electricity with lowered price of about 40% and a reasonable payback period of 11 years. The work confirms that wind energy is cost-effective and a good alternative to reducing global warming.

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Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador

Cited by 68 publications

References 40 publications

One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

Performance assessment of CORDEX regional climate models in wind speed simulations over Zambia

LSTM neural network techniques-based analytical predictive models for wind energy and mechanical power

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