Predicting daily mean solar power using machine learning regression techniques

Jawaid, Faizan; NazirJunejo, Khurum

doi:10.1109/intech.2016.7845051

Cited by 38 publications

(9 citation statements)

References 10 publications

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“…The time series of the total energy consumption, wind and solar power production is used in this study to forecast the future trend of the variables in Germany. The time series dataset is retrieved from the Open Power System Data (OPSD) for Germany, which has been rapidly expanding its renewable energy production in recent years [51]. The temporal resolution of the variables used for the RNN -based prediction is daily.…”

Section: Data Source and Workflowmentioning

confidence: 99%

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Yazdan¹,

Khosravi²,

Saki³

et al. 2022

Preprint

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The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

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Section: Data Source and Workflowmentioning

confidence: 99%

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Yazdan¹,

Khosravi²,

Saki³

et al. 2022

Preprint

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“…Deterministic methods, based on physical events, attempt to forecast PV plant output by utilizing software such as PVSyst and System Advisor Model (SAM) to consider the electrical model of the PV devices that make up the plant. e electrical, thermal, and optical properties of PV modules were modelled using a deterministic method in [11][12][13][14]. e majority of published research on PV power forecasting focuses solely on deterministic forecasting, i.e., point forecasting.…”

Section: Related Literaturementioning

confidence: 99%

“…Insights into the features of data relationships and the relevance of particular qualities in datasets are provided by machine learning [19]. Jawaid et al [11] examined several ANN algorithms without revealing the characteristics of the prediction model or their numerical performance. Several additional studies used machine learning approaches to estimate solar irradiance rather than PV power [20][21][22].…”

Section: Related Literaturementioning

confidence: 99%

Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Yadav

Kannan

Meraj

et al. 2022

Mathematical Problems in Engineering

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Climate change and pollution are serious issues that are driving people to adopt renewable energy instead of fossil fuels. Most renewable energy technologies rely on atmospheric conditions to generate power. Solar energy is a renewable energy source that causes the least environmental damage. Solar energy can be converted to electricity, which necessitates the use of a PV system. This study presents a design, which analyses the output power performance of PV, using machine learning technique in India and Malaysia; using this, we would get the predicted amount of solar power using different weather conditions for both India and Malaysia. This study is divided into two sections, such as the data collection section and the implementation system. Dataset was collected from a weather NASA website, which took various weather parameters, based on which the model will be evaluated. The proposed research work is developed using ANN and is an amalgamation of statistical regression and neural networks, which help the model to get high accuracy by helping the model learn more complex relationships between parameters, which is able to evaluate the output power performance of photovoltaic cells with different environmental condition parameters in India and Malaysia. The ANN models are found to successfully predict PV output power with root mean square error (RMSE) of 1.5565, which was used as a measure of our model’s accuracy. This ANN model also outperforms other models available in the literature. This will have a noteworthy contribution in scaling the PV deployment in countries such as India and Malaysia and will increase the share of PV power in their national power production, as it would give the industry and the two countries an idea as to how the predicted output PV power would vary based on weather conditions, such as temperature.

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“…Because of the non-linearity of the dataset, we used the models indicated above instead of linear models. The most basic and widely used regression method is linear regression (LR) [10].It uses linear predictor functions to represent the relationship between the input and output variables, and a least squares approach is used to estimate the unknown model parameters from the data. A set of linear equations or an iterative method like gradient descent can be used to estimate parameter values.…”

Section: Forecasting Modelsmentioning

confidence: 99%

Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

Anuradha¹,

Erlapally²,

Karuna³

et al. 2021

E3S Web Conf.

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Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

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Predicting daily mean solar power using machine learning regression techniques

Cited by 38 publications

References 10 publications

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

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