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Mostafa Majidpour
Smart Grid Energy Research Center, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA

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Journal article
Published: 17 September 2018 in Forecasting
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In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria.

ACS Style

Mostafa Majidpour; Hamidreza Nazaripouya; Peter Chu; Hemanshu R. Pota; Rajit Gadh. Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System. Forecasting 2018, 1, 107 -120.

AMA Style

Mostafa Majidpour, Hamidreza Nazaripouya, Peter Chu, Hemanshu R. Pota, Rajit Gadh. Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System. Forecasting. 2018; 1 (1):107-120.

Chicago/Turabian Style

Mostafa Majidpour; Hamidreza Nazaripouya; Peter Chu; Hemanshu R. Pota; Rajit Gadh. 2018. "Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System." Forecasting 1, no. 1: 107-120.

Journal article
Published: 01 February 2016 in Applied Energy
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ACS Style

Mostafa Majidpour; Charlie Qiu; Peter Chu; Hemanshu Pota; Rajit Gadh. Forecasting the EV charging load based on customer profile or station measurement? Applied Energy 2016, 163, 134 -141.

AMA Style

Mostafa Majidpour, Charlie Qiu, Peter Chu, Hemanshu Pota, Rajit Gadh. Forecasting the EV charging load based on customer profile or station measurement? Applied Energy. 2016; 163 ():134-141.

Chicago/Turabian Style

Mostafa Majidpour; Charlie Qiu; Peter Chu; Hemanshu Pota; Rajit Gadh. 2016. "Forecasting the EV charging load based on customer profile or station measurement?" Applied Energy 163, no. : 134-141.

Journal article
Published: 25 November 2014 in IEEE Transactions on Industrial Informatics
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This paper proposes a new cellphone application algorithm which has been implemented for the prediction of energy consumption at electric vehicle (EV) charging stations at the University of California, Los Angeles (UCLA). For this interactive user application, the total time for accessing the database, processing the data, and making the prediction needs to be within a few seconds. We first analyze three relatively fast machine learning-based time series prediction algorithms and find that the nearest neighbor (NN) algorithm (k NN with k = 1) shows better accuracy. Considering the sparseness of the time series of the charging records, we then discuss the new algorithm based on the new proposed time-weighted dot product (TWDP) dissimilarity measure to improve the accuracy and processing time. Two applications have been designed on top of the proposed prediction algorithm: one predicts the expected available energy at the outlet and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is approximately 1 s for both applications. The granularity of the prediction is 1 h and the horizon is 24 h; data have been collected from 20 EV charging outlets.

ACS Style

Mostafa Majidpour; Charlie Qiu; Peter Chu; Rajit Gadh; Hemanshu Pota. Fast Prediction for Sparse Time Series: Demand Forecast of EV Charging Stations for Cell Phone Applications. IEEE Transactions on Industrial Informatics 2014, 11, 242 -250.

AMA Style

Mostafa Majidpour, Charlie Qiu, Peter Chu, Rajit Gadh, Hemanshu Pota. Fast Prediction for Sparse Time Series: Demand Forecast of EV Charging Stations for Cell Phone Applications. IEEE Transactions on Industrial Informatics. 2014; 11 (1):242-250.

Chicago/Turabian Style

Mostafa Majidpour; Charlie Qiu; Peter Chu; Rajit Gadh; Hemanshu Pota. 2014. "Fast Prediction for Sparse Time Series: Demand Forecast of EV Charging Stations for Cell Phone Applications." IEEE Transactions on Industrial Informatics 11, no. 1: 242-250.

Conference paper
Published: 01 July 2014 in 2014 IEEE PES General Meeting | Conference & Exposition
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This paper describes the core cellphone application algorithm which has been implemented for the prediction of energy consumption at Electric Vehicle (EV) Charging Stations at UCLA. For this interactive user application, the total time of accessing database, processing the data and making the prediction, needs to be within a few seconds. We analyze four relatively fast Machine Learning based time series prediction algorithms for our prediction engine: Historical Average, k-Nearest Neighbor, Weighted k-Nearest Neighbor, and Lazy Learning. The Nearest Neighbor algorithm (k Nearest Neighbor with k=1) shows better performance and is selected to be the prediction algorithm implemented for the cellphone application. Two applications have been designed on top of the prediction algorithm: one predicts the expected available energy at the station and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is about one second for both applications.

ACS Style

Mostafa Majidpour; Charlie Qiu; Ching-Yen Chung; Peter Chu; Rajit Gadh; Hemanshu R. Pota. Fast demand forecast of Electric Vehicle Charging Stations for cell phone application. 2014 IEEE PES General Meeting | Conference & Exposition 2014, 1 -5.

AMA Style

Mostafa Majidpour, Charlie Qiu, Ching-Yen Chung, Peter Chu, Rajit Gadh, Hemanshu R. Pota. Fast demand forecast of Electric Vehicle Charging Stations for cell phone application. 2014 IEEE PES General Meeting | Conference & Exposition. 2014; ():1-5.

Chicago/Turabian Style

Mostafa Majidpour; Charlie Qiu; Ching-Yen Chung; Peter Chu; Rajit Gadh; Hemanshu R. Pota. 2014. "Fast demand forecast of Electric Vehicle Charging Stations for cell phone application." 2014 IEEE PES General Meeting | Conference & Exposition , no. : 1-5.