Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

Sala, Simone; Amendola, A.; Leva, Sonia; Mussetta, Marco; Niccolai, Alessandro; Ogliari, Emanuele

doi:10.3390/en12234520

Cited by 9 publications

(9 citation statements)

References 32 publications

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“…Nevertheless, even LSTM imposed certain drawbacks, which include training time and accuracy of recognition. Apart from price and/or output forecasting, artificial intelligence methods have been widely used for nowcasting as well (intra-hour forecasts), as explained in [32].…”

Section: Methodsmentioning

confidence: 99%

The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market

et al. 2022

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In this paper, a study was completed investigating the financial viability of a 5 MW solar power plant in Montenegro with direct access to the market, rather than a long-term power purchase agreement. The empirical research included an econometric analysis and forecast of the prices on the exchange market, using two methods, autoregressive integrated moving average (ARIMA) and neural network auto regression (NNAR), which are compared to the forecast electricity prices. The former was used in order to obtain the electricity prices forecast, since it showed significantly better predictive performances. Consequently, the financial analysis results indicated this business strategy is a financially more viable option, even though it implies increased risks. All investigated metrics and sensitivity analysis pointed in favor of this option, which has significantly higher profitability with a shorter payback period, compared to the usual market strategy. The main conclusion and recommendation drawn from the analysis are that taking into account the entire environment and prospects for the following years, a riskier business strategy of entering the market directly, or a so-called structured PPA, is put forward to improve project returns and speed up energy-transformation processes in a developing country.

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Section: Methodsmentioning

confidence: 99%

The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market

et al. 2022

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“…These studies describe ultra-short-, short-, medium-, and long-term forecasts using artificial intelligence methods, most commonly artificial (ANN) or deep neural networks (DNN). Depending on the publication, different types of networks are proposed, such as MLP-multi-layer perceptron [18], MLP ABC-MLP network using the artificial bee colony algorithm [19], RNN-recurrent neural network [20], LSTM network [21], CNN-convolutional neural network [22,23], and hybrid models [16,24]. In addition, other machine learning algorithms are distinguished, such as k-nearest neighbors (KNN), decision trees (DT), LightGBM, CatBoost, and extreme gradient boosting (XGBoost) [25], among others.…”

Section: Literature Reviewmentioning

confidence: 99%

Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data

Sumorek,

Idzkowski

2023

Energies

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This article presents a time series analysis for predicting energy production in photovoltaic (PV) power plant systems, namely fixed and solar-tracking ones, which were located in the north-east of Poland. The purpose of one-day forecasts is to determine the effectiveness of preventive actions and manage power systems effectively. The impact of climate variables affecting the production of electricity in the photovoltaic systems was analyzed. Forecasting models based on traditional machine learning (ML) techniques and multi-layer perceptron (MLP) neural networks were created without using solar irradiance as an input feature to the model. In addition, a few metrics were selected to determine the quality of the forecasts. The preparation of the dataset for constructing the forecasting models was discussed, and some ways for improving the metrics were given. Furthermore, comparative analyses were performed, which showed that the MLP neural networks used in the regression problem provided better results than the MLP classifier models. The Diebold–Mariano (DM) test was applied in this study to distinguish the significant differences in the forecasting accuracy between the individual models. Compared to KNN (k-nearest neighbors) or ARIMA models, the best results were obtained for the simple linear regression, MLPRegressor, and CatBoostRegressor models in each of the investigated photovoltaic systems. The R-squared value for the MLPRegressor model was around 0.6, and it exceeded 0.8 when the dataset was split and separated into months.

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“…In the present work a series of indicator has been defined to evaluate the goodness of the prediction, in order to address different issues. Among the existing performance metrics, we selected the normalized Mean Absolute Error (1) (nMAE [31]) to evaluate the overall performance:…”

Section: Performance Measurementmentioning

confidence: 99%

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

et al. 2020

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The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

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Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

Cited by 9 publications

References 32 publications

The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market

The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market

Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

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