Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

Alsharif, Mohammed H.; Younes, Mohammad K.; Kim, Jeong

doi:10.3390/sym11020240

Cited by 209 publications

(84 citation statements)

References 37 publications

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“…The ARMA model is utilized mainly for stationary time series data. In this method, the predicted variable is calculated on the basis of a linear relationship with its past values [25,26]. In cases when the data is non-stationary and has seasonal characteristics, as will be explained in the next section, it has to be transformed into a stationary one before an ARMA model is fit.…”

Section: Of 15mentioning

confidence: 99%

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

et al. 2020

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The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts.

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Section: Of 15mentioning

confidence: 99%

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

et al. 2020

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“…ARIMA is a term (concept) and expression that stands for Auto Regressive Integrated Moving Average; it is a manner and paradigm that captures a set of different temporal component in time series data, ARIMA is a prediction method that visualizes the future values of a certain series, others call it "Box-Jenkins" [12]. ARIMA is commonly better and more efficient than the exponential smoothing method given that the length of data is moderately and the observations of time series are stationary or stable and the correlation between these observations must exist [12,22].…”

Section: Stochastic Modelsmentioning

confidence: 99%

Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model

Awad¹,

Zaid-Alkelani²

2019

IJISA

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The prediction of future water demand will help water distribution companies and government to plan the distribution process of water, which impacts on sustainable development planning. In this paper, we use a linear and nonlinear models to predict water demand, for this purpose, we will use different types of Artificial Neural Networks (ANNs) with different learning approaches to predict the water demand, compared with a known type of statistical methods. The dataset depends on sets of collected data (extracted from municipalities databases) during a specific period of time and hence we proposing a nonlinear model for predicting the monthly water demand and finally provide the more accurate prediction model compared with other linear and nonlinear methods. The applied models capable of making an accurate prediction for water demand in the future for the Jenin city at the north of Palestine. This prediction is made with a time horizon month, depending on the extracted data, this data will be used to feed the neural network model to implement mechanisms and system that can be employed to predicts a short-term for water demands. Two applied models of artificial neural networks are used; Multilayer Perceptron NNs (MLPNNs) and Radial Basis Function NNs (RBFNNs) with different learning and optimization algorithms Levenberg Marquardt (LM) and Genetic Algorithms (GAs), and one type of linear statistical method called Autoregressive integrated moving average ARIMA are applied to the water demand data collected from Jenin city to predict the water demand in the future. The execution results appear that the MLPNNs-LM type is outperformed the RBFNN-GAs and ARIMA models in the prediction the water demand values.

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“…ARIMA is used in forecasting social, economic, engineering, foreign exchange, and stock problems. It predicts future values of a time series using a linear combination of its past values and a series of errors [23][24][25][26][27]. Since batteries in the data center are always on charging mode, the deep discharge is a rare occurrence for batteries and their distinctive internal chemistry causes different behaviors like stationary or stochastic for each battery.…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Prediction for Batteries in Data Centers

Haider

Zhao

2020

Energies

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Prediction of a battery's health in data centers plays a significant role in Battery Management Systems (BMS). Data centers use thousands of batteries, and their lifespan ultimately decreases over time. Predicting battery's degradation status is very critical, even before the first failure is encountered during its discharge cycle, which also turns out to be a very difficult task in real life. Therefore, a framework to improve Auto-Regressive Integrated Moving Average (ARIMA) accuracy for forecasting battery's health with clustered predictors is proposed. Clustering approaches, such as Dynamic Time Warping (DTW) or k-shape-based, are beneficial to find patterns in data sets with multiple time series. The aspect of large number of batteries in a data center is used to cluster the voltage patterns, which are further utilized to improve the accuracy of the ARIMA model. Our proposed work shows that the forecasting accuracy of the ARIMA model is significantly improved by applying the results of the clustered predictor for batteries in a real data center. This paper presents the actual historical data of 40 batteries of the large-scale data center for one whole year to validate the effectiveness of the proposed methodology.

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Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

Cited by 209 publications

References 37 publications

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model

Cluster-Based Prediction for Batteries in Data Centers

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