Wind power forecasting based on daily wind speed data using machine learning algorithms

Demolli, Halil; Dokuz, Ahmet Şakir; Ecemiş, Alper; Gökçek, Murat

doi:10.1016/j.enconman.2019.111823

Cited by 328 publications

(148 citation statements)

References 32 publications

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“…Solar and wind energy sources are a promising electrical energy sources due to its abundant nature and gradually declining investment costs [3], [4], [30]. The global installed capacity of wind and solar PV energy sources are more than 539 GW [31] and 405 GW [32] by 2017 respectively. Wind and PV energy sources are weather dependent which are mainly affected by wind speed and solar irradiance [5], [16], [31], [32].…”

Section: Grid Connected Hybrid Pv-wind Power System and Its Featuresmentioning

confidence: 99%

“…The global installed capacity of wind and solar PV energy sources are more than 539 GW [31] and 405 GW [32] by 2017 respectively. Wind and PV energy sources are weather dependent which are mainly affected by wind speed and solar irradiance [5], [16], [31], [32]. The power output of wind turbine (P WT ) can be calculated using wind speed data and power curves prepared by wind turbine companies as shown in Fig.…”

Section: Grid Connected Hybrid Pv-wind Power System and Its Featuresmentioning

confidence: 99%

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Voltage Sag Enhancement of Grid Connected Hybrid PV-Wind Power System Using Battery and SMES Based Dynamic Voltage Restorer

Molla

Kuo

2020

IEEE Access

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Renewable energy sources; which are abundant in nature and climate friendly are the only preferable choice of the world to provide green energy. The limitation of most renewable energy sources specifically wind and solar PV is its intermittent nature which are depend on wind speed and solar irradiance respectively and this leads to power fluctuations. To compensate and protect sensitive loads from being affected by the power distribution side fluctuations and faults, dynamic voltage restorer (DVR) is commonly used. This research work attempts to withstand and secure the effect of voltage fluctuation of grid connected hybrid PV-wind power system. To do so battery and super magnetic energy storage (SMES) based DVR is used as a compensating device in case of voltage sag condition. The compensation method used is a pre-sag compensation which locks the instantaneous real time three phase voltage magnitude and angle in normal condition at the point of common coupling (PCC) and stores independently so that during a disturbance it used for compensation. Symmetrical and asymmetrical voltage sags scenario are considered and compensation is carried out using Power System Computer Aided Design or Electro Magnetic Transient Design and Control (PSCAD/EMTDC) software.

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Section: Grid Connected Hybrid Pv-wind Power System and Its Featuresmentioning

confidence: 99%

Section: Grid Connected Hybrid Pv-wind Power System and Its Featuresmentioning

confidence: 99%

Voltage Sag Enhancement of Grid Connected Hybrid PV-Wind Power System Using Battery and SMES Based Dynamic Voltage Restorer

Molla

Kuo

2020

IEEE Access

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“…The characteristics of WPG make it difficult to ensure power system reliability [4,5]. Although various wind power forecasting methods such as the ensemble method [6], aggregated probabilistic method [7], and machine learning-based method [8] have been researched, uncertainty cannot be completely eliminated owing to the nature of wind-resource phenomena. An energy storage system (ESS) plays an essential role in managing the uncertainty of WPG [9].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement-Learning-Based Virtual Energy Storage System Operation Strategy for Wind Power Forecast Uncertainty Management

2020

Applied Sciences

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Uncertainties related to wind power generation (WPG) restrict its usage. Energy storage systems (ESSs) are key elements employed in managing this uncertainty. This study proposes a reinforcement learning (RL)-based virtual ESS (VESS) operation strategy for WPG forecast uncertainty management. The VESS logically shares a physical ESS to multiple units, while VESS operation reduces the cost barrier of the ESS. In this study, the VESS operation model is suggested considering not only its own operation but also the operation of other units, and the VESS operation problem is formulated as a decision-making problem. To solve this problem, a policy-learning strategy is proposed based on an expected state-action-reward-state-action (SARSA) approach that is robust to variations in uncertainty. Moreover, multi-dimensional clustering is performed according to the WPG forecast data of multiple units to enhance performance. Simulation results using real datasets recorded by the National Renewable Energy Laboratory project of U.S. demonstrate that the proposed strategy provides a near-optimal performance with a less than 2%-point gap with the optimal solution. In addition, the performance of the VESS operation is enhanced by multi-user diversity gain in comparison with individual ESS operation.

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“…Indicatively, these include the use of Kalman filters [16], neural networks [17], and combinations of machine learning (ML) methods [18]. Another example of using imperfect weather forecast towards wind power forecasting is the study by [19], who used wind speed and its standard deviation as inputs to machine learning algorithms. Instead of relying on imperfect meteorological weather predictions, some researchers focused on directly estimating the wind speed using variations of autoregressive integrated moving average models [20,21] or artificial neural networks [22].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

2020

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Weather variables are an important driver of power generation from renewable energy sources. However, accurately predicting such variables is a challenging task, which has a significant impact on the accuracy of the power generation forecasts. In this study, we explore the impact of imperfect weather forecasts on two classes of forecasting methods (statistical and machine learning) for the case of wind power generation. We perform a stress test analysis to measure the robustness of different methods on the imperfect weather input, focusing on both the point forecasts and the 95% prediction intervals. The results indicate that different methods should be considered according to the uncertainty characterizing the weather forecasts.

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Wind power forecasting based on daily wind speed data using machine learning algorithms

Cited by 328 publications

References 32 publications

Voltage Sag Enhancement of Grid Connected Hybrid PV-Wind Power System Using Battery and SMES Based Dynamic Voltage Restorer

Voltage Sag Enhancement of Grid Connected Hybrid PV-Wind Power System Using Battery and SMES Based Dynamic Voltage Restorer

Reinforcement-Learning-Based Virtual Energy Storage System Operation Strategy for Wind Power Forecast Uncertainty Management

The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece

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