Time series prediction for output of multi-region solar power plants

Zheng, Jianqin; Zhang, Haoran; Dai, Yuanhao; Wang, Bohong; Zheng, Taicheng; Liao, Qi; Liang, Yongtu; Zhang, Fengwei; Song, Xuan

doi:10.1016/j.apenergy.2019.114001

Cited by 103 publications

(38 citation statements)

References 45 publications

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“…To predict the PV output power, first, the solar irradiation and other metrological variables are predicted in indirect methods whereas, the indirect method solar power is predicted from historical data directly [5]. Moreover, the direct methods are further categorized into Markov chain prediction schemes [6], regression prediction techniques [7], time series prediction techniques [8], and Artificial Neural Network prediction methods [9], etc. Out of these methods, artificial intelligence-based methods, such as neural networks, are broadly used for prediction due to their high accuracy and performance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

et al. 2021

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This paper presents a prediction error-based power forecasting (PEBF) method for a Photovoltaic (PV) system, using Photovoltaics for Utility Scale Applications (PVUSA) model based grey box neural network (GBNN). First, the differential equation based PVUSA model is transformed into a neural network. In the proposed PEBF scheme, the neural network is set to train whenever the difference between predicted and output powers increases from a certain threshold defined based on system dynamics and requirements. The unique design of the PVUSA model based grey box neural network takes far less training time than usual black-box neural network based models. This gives the proposed prediction scheme an advantage of updating the prediction model parameters from frequent training of neural networks with the change in metrological variables. The effectiveness of the proposed prediction scheme is demonstrated by a real case study regarding a 20MW grid-connected PV system located in Dongying city of Shandong province China. To evaluate the efficiency of the developed scheme, different assessment metrics, mean absolute error (MAE), root mean square error (RMSE), weighted mean absolute error (WMAE) and coefficient of determination ( 2 ) are applied. The average values of MAE, RMSE, and WMAE were 0.12 %, 0.20% and 0.23% respectively for all cases. The results demonstrate that the proposed scheme predicts the PV power efficiently within the defined error tolerance level, which shows the effectiveness and feasibility of the proposed prediction scheme. The prediction accuracy of the proposed scheme has been compared with the conventional black box neural network models and reveals outperformed performance with respect to prediction accuracy improvement. The proposed prediction scheme will help to balance power production and demands across integrated networks through economic dispatch decisions between the power sources.INDEX TERMS Solar photovoltaic system, grey box neural network, prediction error based power forecasting scheme, real case study, renewable energy.

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Section: Introductionmentioning

confidence: 99%

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

et al. 2021

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“…When the temperature suddenly changes, the number of people who catch a cold increases significantly, while the number of people who are not urgently ill when there is a heavy rain drops drastically. In the study of solar power grids (Zheng et al 2020), it is found that compared with the general artificial neural network, the model that takes into account the time series can improve the accuracy of prediction. Therefore, in the first step of the model, the prediction part comprehensively considers many factors, which may affect the number of visits for different types of patients in the next week (weather, temperature, the number of patient visits in history) and the number of patients is separately predicted.…”

Section: Problem Definitionmentioning

confidence: 99%

Data-driven hospital personnel scheduling optimization through patients prediction

Feng

Tang

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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With the rapid development of the modern city, technologies of smart cities are indispensable for solving urban problems. Medical services are one of the key areas related to the lives of urban residents. In particular, how to effectively manage the human resources of a hospital is a complex and challenging problem to improve treatment capabilities. Due to the grievous shortage of medical personnel, hospitals have to make quality schedules to improve the efficiency of the hospital and the utilization rate of human resources. Although there have been a large number of researches on hospital staff scheduling, few people also consider future patient population forecasts, doctor scheduling and hospital structure. These factors are very important in the hospital staff scheduling problem. Concerning this, this paper establishes an optimization system combining a two-layer mixed-integer linear programming and an extended prophet model for the hospital personnel scheduling. The model considers factors such as weather, disease types, number of patients, room resources, doctor resources, working hours, etc., and can quickly obtain a timetable with complex constraints. Finally, the convergence and the practicability of the model has been verified with real data from a hospital in China.

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“…It enabled preselection in different cloud movement scenarios; stationary, and ramp, and interpolate cloud information to provide consistent PV nowcasts. Zheng et al (2020) proposed a method to predict the output of solar power plants based on time series forecasting, primarily focusing on multiple regions. They used a Long Short-Term Memory Network (LSTM) model for the forecasting and multiple LSTM structures were compared to determine the final prediction model with sensitivity analysis.…”

Section: Related Work In Solar Irradiance Nowcastingmentioning

confidence: 99%

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

et al. 2021

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The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.

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Time series prediction for output of multi-region solar power plants

Cited by 103 publications

References 45 publications

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

Data-driven hospital personnel scheduling optimization through patients prediction

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

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