Ensemble Machine Learning Forecaster for Day Ahead PV System Generation

Kaffash, Mahtab

doi:10.1109/sege.2019.8859918

Cited by 8 publications

(1 citation statement)

References 15 publications

(13 reference statements)

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“…an ensemble of models). For more details, the readers may refer to Guermoui et al 43 For example, Kaffash and Deconinck 44 proposed a prediction approach based on the combination of ANN and SVR for 1-day ahead PV power generation prediction without using any exogenous inputs. To this aim, the Random Forest (RF) technique was employed for selecting the relevant historical data (i.e.…”

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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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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Solar Photovoltaic System‐Based Power Generation

Pachauri,

Yadav,

Torto

et al. 2024

Clean and Renewable Energy Production

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Data‐driven forecasting of localPVgeneration for stochasticPV‐battery system management

Kaffash

Bruninx

2021

Int J Energy Res

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Summary Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system.

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Ensemble Machine Learning Forecaster for Day Ahead PV System Generation

Cited by 8 publications

References 15 publications

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

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

Solar Photovoltaic System‐Based Power Generation

Data‐driven forecasting of localPVgeneration for stochasticPV‐battery system management

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