Most Influential Variables for Solar Radiation Forecasting Using Artificial Neural Networks

Alluhaidah, B. M.; Shehadeh, Shadi H.; El-Hawary, M.E.

doi:10.1109/epec.2014.36

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…The most influential parameters for global solar radiation (GSR) prediction were studied in [95] for finding the combination of weather parameters that provided the best dayahead GSR prediction using a three-layer feed forward NN model The day, air temperature, pressure, relative humidity, cloud cover, wind speed, and direction were investigated as input data, which was acquired from real three-year outdoor solar radiation data. The performance evaluation based on the RMSE, MAPE, and correlation coefficient have established that humidity, temperature, and cloud-cover are the optimal features in the GSR prediction, while considering reducing the system's complexity in terms of the number of neurons in the hidden layer.…”

Section: ) Neural Network-based Modelsmentioning

confidence: 99%

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

2020

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Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review.

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Section: ) Neural Network-based Modelsmentioning

confidence: 99%

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

2020

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“…In addition, previous studies have used various weather variables to predict solar radiation. Alluhaidah et al (2014) examined studies using various weather variables to identify the root mean square error (RMSE) and mean absolute percentage error (MAPE) and revealed that cloud cover, humidity, and temperature contribute the most to prediction [4]. Kwon et al (2019) attempted to predict the global horizontal irradiance (GHI) using the temperature, relative humidity, dewpoint, and sky-coverage values [5].…”

Section: Introductionmentioning

confidence: 99%

Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model

Kim

Park

Park³

et al. 2023

IEEE Access

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The renewable energy industry is rapidly expanding due to environmental pollution from fossil fuels and continued price hikes. In particular, the solar energy sector accounts for about 48.7% of renewable energy, at the highest production ratio. Therefore, climate prediction is essential because solar power is affected by weather and climate change. However, solar radiation, which is most closely related to solar power, is not currently predicted by the Korea Meteorological Administration; therefore, solar radiation prediction technology is needed. In this study, we predict solar radiation using extra-atmospheric solar radiation and three weather variables: temperature, relative humidity, and total cloud volume. We compared the performance of single models of machine and deep learning in previous work. For the single-model comparison, we used boosting techniques, such as extreme gradient boosting and categorical boosting (CatBoost) in machine learning, and the recurrent neural network (RNN) family (long short-term memory and gated recurrent units). In this paper, we compare CatBoost (previously the best model) with CNN and present a CNN-CatBoost hybrid model prediction method that combines CatBoost in machine learning and CNN in deep learning for the best predictive performance for a single-model comparison. In addition, we checked the accuracy change when adding wind speed and precipitation to the hybrid model. The model that considers wind speed and precipitation improved at all but three (Gangneung, Suwon, and Cheongju) of the 18 locations.INDEX TERMS Solar radiation forecasting, weather variable, categorical boosting (CatBoost), convolutional neural network (CNN), hybrid model (CNN-CatBoost)

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“…Rehman and Mohandes [8] estimated global solar radiation using relative humidity and daily mean temperature and showed that their model outperformed other cases ((1) daily maximum air temperature and (2) daily mean air temperature), with an absolute mean percentage error of 4.49%. Alluhaidah et al [9] compared the impact of seven meteorological variables on the accuracy of predictions, including air temperature, relative humidity, pressure, cloud coverage, wind speed, and wind direction. The best results strongly depended on cloud coverage and relative humidity.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System

et al. 2022

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An increase in renewable energy injected into the power system will directly cause a fluctuation in the overall voltage and frequency of the power system. Thus, renewable energy prediction accuracy becomes vital to maintaining good power dispatch efficiency and power grid operation security. This article compares the one-day-ahead PV power forecasting results of three models paired with three groups of weather data. Since the number, loss, and matching problem of weather data will all influence the training results of the model, a pre-processing data framework is proposed to solve the problem in this study. The models used are a deep learning algorithm-based artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU). The weather data groups are Central Weather Bureau (CWB), local weather station (LWS), and hybrid data (the combination of CWB and LWS data). Compared to the other two groups, hybrid data showed a 5–8% improvement in measurements. In addition, when it comes to different weather conditions, the advantages of the LSTM model were highlighted. After further analysis, the LSTM model combined with hybrid data showed the most accurate measurements, which was proved through forecasting results for one month. Finally, the results indicate that when the amount of data is limited, using hybrid data and the five weather features is helpful for training the model. Accordingly, the proposed model shows better one-day-ahead PV forecasting.

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Most Influential Variables for Solar Radiation Forecasting Using Artificial Neural Networks

Cited by 20 publications

References 22 publications

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

Solar Radiation Forecasting Based on the Hybrid CNN-CatBoost Model

Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System

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