A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Huang, Chia-Yu; Kuo, Ping‐Huan

doi:10.3390/en11102777

Cited by 80 publications

(33 citation statements)

References 49 publications

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“…The authors of [33] propose an ANN-based approach for predicting energy usage of buildings, additionally considering the users' characteristics and their activities as relevant features. In [34,35], an ANN model is proposed for carrying out day-ahead power predictions that on a specific scenario performs better than several other tested methods such as support-vector machine (SVM) and multilayer perceptron (MLP). The results show that the approach is suitable for assessing DR load shifting options based on a time-of-use pricing scheme achieving district level cost savings of around 15%.…”

Section: Related Workmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

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In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

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Section: Related Workmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

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“…The prediction results of the forecasting models are evaluated using the mean absolute error (MAE) [29], root means squire error (RMSE) and the r 2 metrics [30]. r 2 is a statistical performances metric that gives a measure of the closeness between the actual and predicted data.…”

Section: Forecasting Error Metricsmentioning

confidence: 99%

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

et al. 2019

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The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

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“…More examples are presented in [19][20][21][22][23]. In addition, models of this category are also widely used in other fields such as wind speed forecasting [24,25], air pollution forecasting [26], and forecasting in some high-dimensional data [27,28]. Through combinations of different data preprocessing strategies, simple statistical or artificial intelligence forecasting modules, and intelligent optimization algorithms, various hybrid models of this category have been invented.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Wang

Tang

et al. 2019

Energies

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Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

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A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Cited by 80 publications

References 49 publications

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

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