Artificial neural network application in wind forecasting: an one-hour-ahead wind speed prediction

Finamore, Antonella R.; Galdi, Vincenzo; Calderaro, Vito; Piccolo, Antonio; Conio, Gaspare; Grasso, Salvatore

doi:10.1049/cp.2016.0545

Cited by 9 publications

(3 citation statements)

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“…generation capacities and demands) over these extended periods. These models are however associated with significant error margins especially due to the presence of renewable resources and highly fluctuating power consumers in the system [13]. Smaller the time horizon considered, the lower is the associated error of the prediction algorithms.…”

Section: B Literature Reviewmentioning

confidence: 99%

Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks

Srikantha

Kundur

2019

IEEE Trans. on Signal and Inf. Process. over Networks

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Rapid advancements in smart grid technologies have brought about the proliferation of intelligent and actuating power system components such as distributed generation, storage and smart appliance units. Capitalizing fully on the potential benefits of these systems for sustainable and economical power generation, management and delivery is currently a significant challenge due to issues of scalability, intermittency and heterogeneity of the associated networks. In particular, vertically-integrated and centralized power system management is no longer tractable for optimally coordinating these diverse devices at large scale while also accounting for the underlying complex physical grid constraints. To address these challenges, we propose a hierarchical signal processing framework for optimal power flow management whereby the cyber-physical network relationships of the modern grid are leveraged to enable intelligent decision-making by individual devices based on local constraints and external information. Decentralized and distributed techniques based on convex optimization and game theoretic constructs are employed for information exchanges and decision-making at each tier of the proposed framework. It is shown via theoretical and simulation studies that our technique allows for the seamless integration of power components into the grid with low computational and communication overhead while maintaining optimal, sustainable and feasible grid operations.

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Section: B Literature Reviewmentioning

confidence: 99%

Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks

Srikantha

Kundur

2019

IEEE Trans. on Signal and Inf. Process. over Networks

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“…The authors in [28] proposed a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. In [29], the author developed a feed-forward neural network approach for wind power generation forecasting to improve the wind forecasting accuracy. However, the wind power forecast is relatively complex, and the forecast errors cannot be avoided.…”

Section: Wind Power Generation Modelmentioning

confidence: 99%

Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach

Yang

et al. 2017

Energies

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This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE) generation (wind power) determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profit, and then the consumers determine their energy demands to maximize their payoffs. The hierarchical game is established between the energy provider and the consumers. The energy provider is the leader and the consumers are the followers in the hierarchical game. We consider two types of consumers according to their response to the price, i.e., the price-taking consumers and the price-anticipating consumers. We derive the equilibrium point of the hierarchical game through the backward induction method. Comparing the two types of consumers, we study the influence of the types of consumers on the equilibrium point. In particular, the uncertainty of the energy supply from the energy provider is considered. Simulation results show that the energy provider can obtain more profit using the proposed decision-making scheme.

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“…Statistical models use historical data only, and outperform physical models in short-term forecasting [10]. Various models for wind speed and wind power forecasting are mushrooming in recent years, among which auto-regressive and moving average (ARMA) [11], artificial neural network (ANN) [12] and support vector regression (SVR) [13] are most widely used. The main advantages are that they compute the forecast results quickly, and can work on personal computers.…”

Section: Introductionmentioning

confidence: 99%

Short-term local prediction of wind speed and wind power based on singular spectrum analysis and locality-sensitive hashing

Liu

et al. 2018

J. Mod. Power Syst. Clean Energy

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With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed, which is based on singular spectrum analysis (SSA) and locality-sensitive hashing (LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend, which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted for prediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.

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Artificial neural network application in wind forecasting: an one-hour-ahead wind speed prediction

Cited by 9 publications

References 0 publications

Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks

Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks

Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach

Short-term local prediction of wind speed and wind power based on singular spectrum analysis and locality-sensitive hashing

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