Online short-term forecasting of photovoltaic energy production

Rashkovska, Aleksandra; Novljan, Jost; Smolnikar, Miha; Mohorčič, Mihael; Fortuna, Carolina

doi:10.1109/isgt.2015.7131880

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Considering, specifically, predictive approaches tailored for smart grids, one of the most important predictive tasks is that of renewable energy forecasting for a network of plants. In this context, machine learning approaches typically aim to find a mapping between historical and forecasted variables [30] [31].…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data

2019

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Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data

2019

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“…During the last years, the forecast of PV energy production has received significant attention since photovoltaics are becoming a major source of renewable energy for the world. Forecast may apply to a single renewable power generation system [20], or refer to an aggregation of large numbers of systems spread over an extended geographic area [3] [19]. Accordingly, different forecasting methods are used.…”

mentioning

confidence: 99%

“…Statistical approaches may or may not take into account NWP data. Some of them are based on time series [8], while others learn adaptive models from data, like autoregressive (AR) models [3], artificial neural networks (ANNs) [20], or SVM classifiers [21]. In this respect, it has been noted that physical property behavior (e.g.…”

mentioning

confidence: 99%

“…Despite the existence of such data mining algorithms applied in renewable energy power forecasting for learning adaptive models [3][4] [20] [21], there is no consensus about the spatio-temporal information to be taken into account, the learning setting to be considered and the learning algorithms to be used. This paper considers all these aspects as dimensions of analysis and investigates their real contribution in renewable energy power forecasting.…”

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confidence: 99%

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Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction?

Ceci

Corizzo

Fumarola

et al. 2017

IEEE Trans. Ind. Inf.

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In this paper, we tackle the problem of power prediction of several photovoltaic (PV) plants spread over an extended geographic area and connected to a power grid. The paper is intended to be a comprehensive study of one-day ahead forecast of PV energy production along several dimensions of analysis: i) The consideration of the spatio-temporal autocorrelation, which characterizes geophysical phenomena, to obtain more accurate predictions. ii) The learning setting to be considered, i.e. using simple output prediction for each hour or structured output prediction for each day. iii) The learning algorithms: We compare artificial neural networks, most often used for PV prediction forecast, and regression trees for learning adaptive models. The results obtained on two PV power plant datasets show that: taking into account spatio/temporal autocorrelation is beneficial; the structured output prediction setting significantly outperforms the non-structured output prediction setting; and regression trees provide better models than artificial neural networks.

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“…Historical solar radiation and atmospheric pressure are successfully used to get 3.78% RMSE and 12.83 MAE. Another technique [15] is mentioned in literature for 6 h ahead the backpropagation neural network-based solar power predictor; authors performed correlation analysis on several weather parameters and finally solar radiation and air temperature are used to develop predictor for solar power forecast, and the proposed method is deployed as a software package at Ljubljana, Slovenia, for testing and validation. Autoregressive exogenous ARX model is successfully integrated with numerical weather prediction which has 35% more accuracy than persistence models in [16]; AR works as a short-term predictor and the model can predict 4 h to several days of solar thermal power.…”

Section: Introductionmentioning

confidence: 99%

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

Rafique

Zhang

Rafique

et al. 2018

International Journal of Photoenergy

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The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the forecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is developed to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for renewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster are expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is carried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy management problem with more accuracy and robustness.

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Online short-term forecasting of photovoltaic energy production

Cited by 6 publications

References 12 publications

Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data

Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data

Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction?

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

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