http://item.bettergrids.org/handle/1001/534 University of California, Riverside Wed, 08 Apr 2026 21:19:03 GMT 2026-04-08T21:19:03Z http://item.bettergrids.org/handle/1001/540 Title: Resource Forecasting Algorithm Authors: Howlader, Abdul Motin, Hamed Mohsenian-Rad Abstract: =============================================================================================== Resource Forecasting Abdul Motin HOwlader and Hamed Mohsenian-Rad University of California, Riverside July 2019 =============================================================================================== ---------------------------------4h-ahead of prediction---------------------------------------- Python File: Forecast_CE-CERT_4h_Pred.py To run this Python File: Python Version 3.6.3 and Keras API are required. Deep neural network method such as Long short-term memory (LSTM) was applied to forecast the PV power. The Python File reads all the input parameters from the "CE-CERT_1200_4h.csv" for LSTM network. Output Results: 4h-ahead of PV power forecasting and it will be saved in the "file_path.csv" file. An actual and prediction graph will be displayed. Percentage normalize root mean square error (%nRMSE) will be shown as an output result. In output "file_path.csv", the first column refers as number of data point, second column for actual data, and third column for prediction data. ---------------------------------24h-ahead of prediction---------------------------------------- Python File: Forecast_CE-CERT_24h_Pred.py To run this Python File: Python Version 3.6.3 and Keras API are required. Deep neural network method such as Long short-term memory (LSTM) was applied to forecast the PV power. The Python File reads all the input parameters from the "CE-CERT_1200_24h.csv" for LSTM network. Output Results: 24h-ahead of PV power forecasting and it will be saved in the "file_path.csv" file. An actual and prediction graph will be displayed. Percentage normalize root mean square error (%nRMSE) will be shown as an output result. In output "file_path.csv", the first column refers as number of data point, second column for actual data, and third column for prediction data. Fri, 06 Sep 2019 00:00:00 GMT http://item.bettergrids.org/handle/1001/540 2019-09-06T00:00:00Z http://item.bettergrids.org/handle/1001/539 Title: Scenario Analysis of Extremum Seeking for Volt-Var Control and Phase Balancing Authors: MacDonald, Jason; Sankur, Michael Abstract: This is a self contained simulation that implements Extremum Seeking Control to provide a selectable objective (Voltage Regulation, Minimize Losses, & Voltage and Current Balancing) to a distribution system. Three phase power flow is solved via the Newton Raphson method. Description: Devices in the network providing extremum seeking control will minimize the selected objective by probing the system with unique sinusoids. The objective will be formed from the appropriate measurements of the system states, as determined via the 3-phase power flow results. The ES controllers extract their gradient based on the probe from the objective values and drive their set point (without the probe) toward the system minimum. See Readme for more description. Tue, 01 Jan 2019 00:00:00 GMT http://item.bettergrids.org/handle/1001/539 2019-01-01T00:00:00Z http://item.bettergrids.org/handle/1001/538 Title: Topology Reconfiguration for Loss Minimization with PV Authors: MacDonald, Jason Abstract: Minimize losses through topology reconfiguration and optimal real/reactive power scheduling from PV in system. Performs an initial OPF on the base network, choosing optimal PV output, then iterates between an exhaustive search for network reconfiguration via swapping the states of an open and closed switch and the OPF until convergence criteria is reached. Description: See Readme in file Tue, 01 Jan 2019 00:00:00 GMT http://item.bettergrids.org/handle/1001/538 2019-01-01T00:00:00Z http://item.bettergrids.org/handle/1001/537 Title: Distribution System State Estimation Algorithm Authors: Izadi, Milad; Mohammad, Farajollahi; and Hamed, Mohsenian-Rad Abstract: =============================================================================================== Distribution System State Estimation Milad Izadi, Mohammad Farajollahi, and Hamed Mohsenian-Rad University of California, Riverside July 2019 =============================================================================================== MATLAB [V_est] = DSSE_UCR(loc_pmu,loc_linesensor,ErrV,ErrI,ErrS) Input Arguments: 1) Location of micro-PMUs and line sensros; 2) Standard deviation of micro_PMUs, line sensor, and psuedo-measurement; 3) Network data including load data (available at loadata.mat) and branch data (available at Ybus.mat,Yfbrn.mat, and Ytbrn.mat). Output Arguments: Complex estimated voltage at all nodes Use the following MATLAB codes and sample input data to call the DSSE_UCR function (a test code is provided in Test_DSSE_UCR.mfile): loc_pmu = [1,12,28]; % Location of micro-PMU loc_linesensor = [ 18 22 7 30 15]; % Location of line sensor ErrV = 1; ErrI = 1; ErrS = 25; % Standard deviation [V_est] = DSSE_UCR(loc_pmu,loc_linesensor,ErrV,ErrI,ErrS) Sun, 28 Jul 2019 00:00:00 GMT http://item.bettergrids.org/handle/1001/537 2019-07-28T00:00:00Z