A double-stage hierarchical hybrid PSO-ANN model for short-term wind power prediction

Eseye, Abinet Tesfaye; Zhang, Jianhua; Zheng, Dehua; Li, Han; Jingfu, Gan

doi:10.1109/icccbda.2017.7951963

Cited by 18 publications

(9 citation statements)

References 16 publications

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“…In (Macedo, Franco, and Romero et al 2017) focused on the spill of renewable energy, particularly photovoltaic systems, and introduced a method for the optimal allocation to avoid the waste of renewable energy. Reference (Eseye, Zhang, and Zheng et al 2017) proposed a model based on an artificial neural network for short-term wind power prediction. Word about cost, from (Jannati, Hosseinian, and Vahidi 2016) and (Kiymaz and Yavuz 2016) focused on the cost of wind power, the former focusing on the reduction of the investment cost of Battery Energy Storage System (BESS), and the latter aiming to design wind power systems with maximum renewable fraction and lowest cost.…”

Section: Word Cloudmentioning

confidence: 99%

Application of machine learning algorithms in wind power: a review

Deng

Xue-hua

Zhou

et al. 2021

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Literature review is an overview of existing research, it can also be used to understand research trends and directions. In recent years, the literature associated with wind power has grown rapidly, and it seems inadequate to rely on human resources to study all papers. Very few studies have used machine learning algorithms and visualization approaches to analyze the trends and directions in wind power. To explore these, we collected 50,579 articles (2012-2019) from Web of Science Core Collection (WoSCC) and 785 papers (2012-2019) from China National Knowledge Infrastructure (CNKI). We applied machine learning algorithms including text mining, word segmentation, T-Distributed Stochastic Neighbor Embedding (T-SNE), Auto-Encoder (AE), visual imagery and other methods to analyze and visually display literature in the field of wind power via analysis of the trends with time-sequence, hotspots in abstracts and keywords, and spatial distribution. China, the United States, and Iran are the top three countries in the field of wind power. Through analyzing the trends between 2012-2019, we find that research hotspots have changed. The usage rate of terms such as Power Generation Control, Power Grids, Wind Power Plants, and Wind Turbines has significantly increased, and the corresponding growth rates are 10.91%, 7.06%, 6.28% and 4.33%, respectively. This study also provides information on the relationship of words of abstracts in papers, which shows that these words are mainly divided into four categories: forecasting, optimization, investment, energy and equipment. An implication of this study is that machine learning algorithms may play an important role in the analysis of wind power literature.

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Section: Word Cloudmentioning

confidence: 99%

Application of machine learning algorithms in wind power: a review

Deng

Xue-hua

Zhou

et al. 2021

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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“…In this work, a different approach is presented, which uses the ETA model built with multiple neural network approaches for environmental prediction, such as solar radiation forecast [21]. Similar to this, forecasting algorithms using NWP [11,29], PSO-ANN integrated NWP [30] for forecasting weather parameters is considered.…”

Section: Related Workmentioning

confidence: 99%

“…Different ANN based techniques [10,33], traditional evolutionary algorithms [34], support vector models [16], and few hybrid AI models [18,30,35] are used for forecasting the output power as illustrated in the comparison study listed below. In addition, the use of evolutionary algorithms like genetic algorithm (GA), DE, and PSO for addressing the optimization of forecasting is investigated [30]. In most of the solar power forecasting algorithms highlighted in the literature, the historical weather data used to train the system were obtained from a weather station situated far away from the actual PV system being studied.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

et al. 2018

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The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

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“…Predominantly, the stochasticity and instability of RESs can be overwhelmed through accurate forecasting and effective storage and utilization. A number of solutions for renewable generation forecasting have been proposed in the literature such as the hybrid of wavelet transform, particle swarm optimization and support vector machines [1], integration of particle swarm optimization and neural networks [2], hybrid of genetic algorithm and neuro-fuzzy systems [3] and others. Similarly, several solutions for renewable generation storage have been devised in the literature, for example using vanadium redox flow battery (VRB) [4], pumped storage hydro units [5], multiple energy storage units [6], or compressed-air energy storage [7] to smooth the instability of renewable generation.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Flexibility of Renewable Energy Integrated Buildings for Optimal Day-ahead and Real-time Power Bidding Considering Batteries and EVs as Demand Response Resources

Eseye

Lehtonen

Tukia

et al. 2019

2019 16th International Conference on the European Energy Market (EEM)

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This study examines the flexibility potential of energy demand resources in buildings. The building flexible demand resources considered are electric vehicles (EVs) and energy storage batteries. The paper investigates the combined optimization of EVs and batteries with the objective of maximizing the total profit of building microgrids in dayahead and regulation (real-time) electricity markets. The major contribution of the paper is the exploitation of the energy flexibility of buildings using EVs as dynamic energy storage device and batteries as manageable demand facility with possibilities of advancing or delaying their consumptions. The proposed optimization objective takes into account EV driving patterns, penalties for renewable energy curtailment, involuntary load shedding and bid imbalance in an explicit optimization setup. The proposed optimization problem is formulated as a dual-step mixed-integer linear programming (MILP) problem, and solved using the CPLEX solver. A number of simulation results are provided to demonstrate the effectiveness of the proposed optimization framework using real data of building electricity consumption and local renewable energy production in the Otaniemi area of Espoo, Finland. We reveal that the devised optimization solution achieves considerable saving in electricity bills, increase profit, reduce renewable energy curtailment, and smoothen peak electricity consumption, compared to a non-optimized operation.

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A double-stage hierarchical hybrid PSO-ANN model for short-term wind power prediction

Cited by 18 publications

References 16 publications

Application of machine learning algorithms in wind power: a review

Application of machine learning algorithms in wind power: a review

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

Exploiting Flexibility of Renewable Energy Integrated Buildings for Optimal Day-ahead and Real-time Power Bidding Considering Batteries and EVs as Demand Response Resources

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