Data centre day-ahead energy demand prediction and energy dispatch with solar PV integration

Ajayi, Oluwafemi; Heymann, Reolyn

doi:10.1016/j.egyr.2021.06.062

Cited by 21 publications

(5 citation statements)

References 45 publications

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“…The normalized mean bias error (NMBE) and coefficient of variation of mean square error (CVRMSE) commonly used in the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guideline 14-2014 were used to evaluate the model's errors. The calculation method is shown in Equations ( 1) and (2).…”

Section: Verification Of Data Center Modelmentioning

confidence: 99%

“…Data centers are high-energy-consuming buildings. According to statistics, the data center industry accounts for 2% of the world's total energy consumption [2], and a data center's average power consumption is about 30 times that of a standard office [3]. As the number of data centers further increases, the energy consumption of data centers doubles every five years [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Simulation and experimental research on the optimization of airflow organization and energy saving in data centers using air deflectors

Ma,

Liu,

et al. 2024

Clean Energy Sci. Technol.

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The airflow organization of the data center directly affects the temperature control performance and the energy efficiency of the cooling equipment. The servers at the bottom of the rack usually suffer from insufficient airflow rate and poor cooling effect. This is because of the limited distance between the bottom servers and the perforated floor, and the small horizontal velocity of the supply air flow. This study aims to improve the uniformity of the cooling airflow in the vertical direction of the rack by the air deflectors, thereby further improving the overall airflow organization in the data center. The size and installation of the deflectors in the data center were optimized according to both the experiment and numerical simulation results. From the results, it is recommended to install the deflector with a width of 100 mm at an angle of 45° under the perforated floor for the rack with the single-side airflow supply. For the rack with the double-side airflow supply, the width of the deflector should be 100 mm and installed at an angle of 30° to the perforated floor to achieve the best airflow distribution. Consequently, the intake airflow rate for the bottom servers significantly increased, and the occurrence of the local hot spots was effectively reduced. The numerical simulation of the airflow organization with and without the deflector was conducted by ANSYS. The results show that, the installation of the deflectors increased the inlet airflow rate for the rack by 16.98% and improved the energy efficiency of the dater center air conditioners by 1.98%.

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Section: Verification Of Data Center Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation and experimental research on the optimization of airflow organization and energy saving in data centers using air deflectors

Ma,

Liu,

et al. 2024

Clean Energy Sci. Technol.

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“…The time series methods included different methods, such as Kalman filter, 19 Support Vector Regression (SVR), 20 Grey Forecasting Method, 21 Auto-Regressive Integrated Moving Average (ARIMA), 22 and Hidden-Markov Models (HMM). 23 The learning methods comprised the Artificial Neural Network (ANN), 24,25 Support Vector Machine (SVM), 26 Wavelet Analysis (WA), 27 and Fuzzy Logic (FL). 28 The ANN is deemed one of the most popular statistical methods adopted to predict the PV generation with a prediction horizon of 24-h ahead.…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Al‐Dahidi

Muhsen

Sari

et al. 2022

Advances in Mechanical Engineering

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The world is becoming more reliant on renewable energy sources to satisfy its growing energy demand. The primary disadvantage of such sources is their significant uncertainty in power production. As appropriate energy production planning and scheduling necessitate a solid and confident assessment of renewable power production, the necessity for developing reliable prediction models grows by the day. This paper proposes an adaptive approach-based ensemble for 1-day ahead production prediction of solar Photovoltaic (PV) systems. Different ensembles of Artificial Neural Networks (ANNs) prediction models are established, whose architectures (number of the ANNs that comprise the ensembles) and configurations (number of hidden nodes required by the ANNs models of the ensembles) change adaptively at each hour h, h∈ [1, 24] of a day, for accommodating the hour seasonality in the solar PV data and, thus, enhancing the 1 day-ahead predictions accuracy. The suggested approach is tested on a 264 kW solar PV system installed at Applied Science Private University, Jordan. Its prediction performance is evaluated, particularly for different weather conditions (seasons) experienced by the concerned PV system, using standard performance metrics. Results show the effectiveness of the suggested approach in predicting solar PV power production and its superiority compared to another prediction approach of the literature that uses single ANNs at each hour h of the day. Specifically, for 1-day ahead prediction, the obtained enhanced accuracy, on average, was around 8%–10% on the test “unseen” datasets.

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“…Mohana et al (2021) employed machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings [44]. Ajayi and Heymann (2021) modelled a novel Marine Predators Algorithm for both training an Artificial Neural Network model used for predicting the energy demand and PV output power [45]. developed two neural networks with different training ranges to replace the whole neural network for predicting I-V curves, P-V curves, and maximum power [46].…”

Section: Plos Onementioning

confidence: 99%

“…• Occurrences of global minima and stagnation issues [3][4][5][6][7] • Scalability problems on the normalization procedures adopted [2,8,[12][13][14][15][16][17] • Over-fitting and under-fitting issues [5, 6, 9-11, 23, 48, 51] • Dimensionality constraints of the solar farm data and data handling issues [18][19][20][21][22][23][24] • Elapsed training time [29,31,37] • Data extraction problems in regression based ML models [10][11][12][13][14][15] • Higher number of trainable parameters in DL models [1, 14, 19-20, 26, 27, 43, 47] • Repetitive training of deep neural networks [19,20,26,27] • High computational overhead due to repetitive process [29][30][31][32][33][34][35][36] • Few predictor models with high complexity and data redundancy [45][46][47]…”

Section: Challengesmentioning

confidence: 99%

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

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A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy–Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.

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Data centre day-ahead energy demand prediction and energy dispatch with solar PV integration

Cited by 21 publications

References 45 publications

Simulation and experimental research on the optimization of airflow organization and energy saving in data centers using air deflectors

Simulation and experimental research on the optimization of airflow organization and energy saving in data centers using air deflectors

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

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