Solar PV Power Forecasting Using Traditional Methods and Machine Learning Techniques

Alam, Ahmed Manavi; Masood, Nahid‐Al; Razee, Iqbal Asif; Mohammad, Zunaed,

doi:10.1109/kpec51835.2021.9446199

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…The main advantage of ARIMA lies in its simplicity. AI methods, in the majority, require data related to the physical nature of the problem such as not only solar irradiance, but also, e.g., air temperature, humidity, wind direction and speed [44], rainfall, cloud cover, elevation, and azimuthal angle [66] and even albedo, vorticity, evaporation, and more [30]. ARIMA uses only previous data and reveals the series structure.…”

Section: Background Literaturementioning

confidence: 99%

ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations

Chodakowska

Nazarko

et al. 2023

Energies

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The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting for the effective integration of solar energy into the energy system. Reliable solar radiation forecasting has become crucial for the design, planning, and operational management of energy systems, especially in the context of ambitious greenhouse gas emission goals. This paper presents a study on the application of auto-regressive integrated moving average (ARIMA) models for the seasonal forecasting of solar radiation in different climatic conditions. The performance and prediction capacity of ARIMA models are evaluated using data from Jordan and Poland. The essence of ARIMA modeling and analysis of the use of ARIMA models both as a reference model for evaluating other approaches and as a basic forecasting model for forecasting renewable energy generation are presented. The current state of renewable energy source utilization in selected countries and the adopted transition strategies to a more sustainable energy system are investigated. ARIMA models of two time series (for monthly and hourly data) are built for two locations and a forecast is developed. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can contribute to the stable long-term integration of solar energy into countries’ systems. However, it is crucial to develop location-specific models due to the variability of solar radiation characteristics. This study provides insights into the use of ARIMA models for solar radiation forecasting and highlights their potential for supporting the planning and operation of energy systems.

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Section: Background Literaturementioning

confidence: 99%

ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations

Chodakowska

Nazarko

et al. 2023

Energies

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“…In an early study, a physical model that considers the relationship between insolation and solar power generation among the above factors was studied first [7], which approximates the insolation with a model that calculates power generation through the rotation of the earth and the equivalent circuit of the PV cell. Since then, statistical prediction models using traditional forecasting techniques such as autoregressive moving average (ARMA) [8] and multiple linear regression (MLR) [9], have been proposed [10]. However, traditional solar forecasting through such modeling lacks adaptability to weather changes, and it is difficult to accurately predict how much solar power will be generated under different weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants

et al. 2023

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Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%.

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“…In order to enhance and more accurately estimate the energy produced by PV panels over the long term, a numerical weather prediction system is employed as an input. Machine learning will be used in this study, and it will use a variety of time series [12]. e accuracy of the energy projection in relation to the allotted time is essential for this task.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach on Time Series for PV-Solar Energy

Sivakumar

Neeraja²,

Rani³

et al. 2022

Advances in Materials Science and Engineering

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Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.

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Solar PV Power Forecasting Using Traditional Methods and Machine Learning Techniques

Cited by 12 publications

References 6 publications

ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations

ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations

Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants

Machine Learning Approach on Time Series for PV-Solar Energy

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