Abstract
Most solar applications and systems can be reliably used to generate electricity and power in many homes and offices. Recently, there is an increase in many solar required systems that can be found not only in electricity generation but other applications such as solar distillation, water heating, heating of buildings, meteorology and producing solar conversion energy. Prediction of solar radiation is very significant in order to accomplish the previously mentioned objectives. In this paper, the main target is to present an algorithm that can be used to predict an hourly activity of solar radiation. Using a dataset that consists of temperature of air, time, humidity, wind speed, atmospheric pressure, direction of wind and solar radiation data, an Artificial Neural Network (ANN) model is constructed to effectively forecast solar radiation using the available weather forecast data. Two models are created to efficiently create a system capable of interpreting patterns through supervised learning data and predict the correct amount of radiation present in the atmosphere. The results of the two statistical indicators: Mean Absolute Error (MAE) and Mean Squared Error (MSE) are performed and compared with observed and predicted data. These two models were able to generate efficient predictions with sufficient performance accuracy.
Highlights
With the rise in technological advancements in our digital modern world, comes the rise in demand for electricity [1]
Using a dataset that consists of temperature of air, time, humidity, wind speed, atmospheric pressure, direction of wind and solar radiation data, an Artificial Neural Network (ANN) model is constructed to effectively forecast solar radiation using the available weather forecast data
Solar radiation data is significant in various sectors such as in conversion and generation of energy from sunlight, water heating, water distillation and meteorology [3, 4]
Summary
With the rise in technological advancements in our digital modern world, comes the rise in demand for electricity [1]. One of the few statistical models such as the exponentially weighted moving average, known as EWMA in short, is most commonly used Other models such as Artificial Neural Network using regression techniques are used from a given set of past samples with the expected conditions, while Markov and Auto-Regressive models are used for unsupervised training and predictions. Unsupervised Models Sometimes, the conditional expectations from past samples are not known, find the optimal solution is hard to control For this reason, and the fact that these are generally non-linear, the methods for designing a model to predict from such samples are tackled using Auto-Regressive Models [10], Markov Models [11] and Auto-Regressive moving average models, or ARMA. HI-SEAS is an environment located on a remote site on the Mauna Loa side of the saddle area on the Big Island of Hawaii in around 8100 feet above sea level [12]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
