Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data

Lago, Jesus; Brabandere, K. De; Ridder, Fjo De; Schutter, Bart De

doi:10.1016/j.solener.2018.07.050

Cited by 59 publications

(34 citation statements)

References 39 publications

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“…Recently, TPE has been put forward to address the limitation of the conventional BO approaches in working with categorical and conditional parameters, and, thus, to improve the hyperparameters selection process [97]. It has, then, been widely used to tune machine learning models in various applications, such as image processing [96], [98]- [101], electricity price forecasting [102], solar irradiance forecasting [103], rail defect prediction [104], occupational accident prediction [105].…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Nguyen

Liu

Zio

2020

Applied Soft Computing

110

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Developing an accurate and reliable multi-step ahead prediction model is a key problem in many Prognostics and Health Management (PHM) applications. Inevitably, the further one attempts to predict into the future, the harder it is to achieve an accurate and stable prediction due to increasing uncertainty and error accumulation. In this paper, we address this problem by proposing a prediction model based on Long Short-Term Memory (LSTM), a deep neural network developed for dealing with the longterm dependencies in time-series data. Our proposed prediction model also tackles two additional issues. Firstly, the hyperparameters of the proposed model are automatically tuned by a Bayesian optimization algorithm, called Tree-structured Parzen Estimator (TPE). Secondly, the proposed model allows assessing the uncertainty on the prediction. To validate the performance of the proposed model, a case study considering steam generator data acquired from different French nuclear power plants (NPPs) is carried out. Alternative prediction models are also considered for comparison purposes.

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Section: Hyperparameter Optimizationmentioning

confidence: 99%

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Nguyen

Liu

Zio

2020

Applied Soft Computing

110

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show abstract

“…It has outperformed other machine learning techniques in image recognition [49,50], speech recognition [51,52], natural language understanding [53], language translation [54,55], particle accelerator data analysis [56], potential drug molecule activity prediction [57], and brain circuit reconstruction [58]. However, its application in the solar irradiance forecasting is limited [59].…”

Section: Deep Learningmentioning

confidence: 99%

Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

2019

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In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN.

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“…For our work, the marginal CDFs of the solar irradiance are built using a point forecast that considers weather information (as done in [27]), and then using quantile regression to build the CDFs of the errors of the point forecasts. For the temperature, we use the same procedure.…”

Section: B Generating Scenariosmentioning

confidence: 99%

Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building

Pippia

Lago

Coninck

et al. 2019

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

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In the context of building heating systems control in office buildings, the current state-of-the-art applies either a deterministic Model Predictive Control (MPC) controller together with a nonlinear model, or a linearized model with a stochastic MPC controller. Deterministic MPC considers only one realization of the external disturbances, which can lead to a low performance solution if the forecasts of the disturbances are not accurate. Similarly, linear models are simplified representations of the building dynamics and might fail to capture some relevant behavior. In this paper, we improve upon the current literature by combining these two approaches, i.e. we adopt a nonlinear model together with a stochastic MPC controller. We consider a scenario-based MPC (SBMPC), where many realizations of the disturbances are considered, so as to include more possible future trajectories for the external disturbances. The adopted scenario generation method provides statistically significant scenarios, whereas so far in the current literature only approximate methods have been applied. Moreover, we use Modelica to obtain the model description, which allows to have a more accurate and nonlinear model. Lastly, we perform simulations comparing standard MPC vs SBMPC vs an optimal control approach with measurements of the external disturbances, and we show how our proposed scenario-based MPC controller can achieve a better performance compared to standard deterministic MPC.

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Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data

Cited by 59 publications

References 39 publications

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

Scenario-based Model Predictive Control Approach for Heating Systems in an Office Building

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