Browsing by Author "Salami, M.J.E"

Now showing 1 - 5 of 5
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    Consumer Load Prediction Based on NARX for Electricity Theft Detection
    (IEEE, 2016) Abdullateef, A. I; Salami, M.J.E; Tijani, B.I
    A range of load prediction techniques has largely been used for energy management at various levels. However, the data used for the prediction are cumulative energy data, which reveal the activities of consumers and not individual consumer, on the distribution power network. Individual consumer data is essential for real time prediction, monitoring and detect of electricity theft. A new approach of monitoring individual consumer based on consumer load prediction using nonlinear autoregressive with eXogenous input (NARX) network is considered in this study. One month average energy consumption data acquired from consumer load prototype developed was used. Consequently, 5-minute step ahead load prediction was achieved. The NARX architecture was based on nine hidden neurons and two tapped delay and the network trained using Bayesian regulation backpropagation technique. The data set contains a total of 8928 data points representing energy consumed at five minute interval for one month. The data was divided into two sets at ratio 70:30 for training and validation, respectively. The training data equals 6206 while the validation data is 2722. MATLAB environment was used for the processing of the data. The training and validation MSE is 0.0225 and 0.0533 respectively, while the total time for the training is 0.016s.
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    Intelligent Technique for Electricity Theft Identification Using Autoregressive Model.
    (LAUTECH Journal of Engineering and Technology, Faculty of Engineering Ladoke Akintola University, Ogbomosho. Available online at, 2018) Abdullateef, A.I; Salami, M.J.E; Akorede, M. F
    Various studies have investigated electricity theft, an illegally act, perpetrated to the detriment of the electricity power providers, however, less attention has been given to identification of the types of electricity theft. Data were acquired from the Consumer Load Prototype developed at two different levels using Sensor- A connected to the Pole Terminal Unit and Sensor-B connected to the Consumer Terminal Unit. The output of the sensors were connected to BNC-2110 device and linked to the PCI 6420E channel, which log the data in the computer for further analysis. LABVIEW (2012) software was programmed to acquires data at a sampling frequency of 500Hz and decimated at 10s interval before logging into the computer hard disk. The feature extraction of the data acquired was achieved using autoregressive technique and model order selectionwas based on minimum description length. The model coefficient AR (20), data acquired and predicted data were used for theft identification. Meter-bypassing theft was identified when the energy consumption from sensor A and sensor B are different, however sensor B reads zero value and there are disparities in the model coefficients. Illegal connection before the meter theft was identified whenever there is difference in energy consumption as evaluated form sensor A and sensor B and there is no zero value recorded from sensor B, while Meter tampering was detected when the energy consumption as evaluated form sensor A and sensor B are different and there are no disparities in the model coefficients.
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    Modified Narx Network for Low Voltage Consumer Load Prediction
    (Zaria Journal of Electrical Engineering Technology,, 2019) Abdullateef, A. I; Salami, M.J.E; Tijani, I.B; Ajala, M.T
    This paper is a continuation of our previous work on Nonlinear autoregressive with eXogenous input (NARX) for load prediction. Application of NARX network in real-time might be difficult since the tapped delay was selected by trial and error, leading to nine hidden neurons which makes the network complex. This NARX network was modified based on Genetic Algorithm (GA) and Differential Evolution (DE) resulting in a new model coded NARX-DE-GA. GA and DE search for the number of hidden neurons and tapped delay automatically. The NARX-DE-GA was used to predict the consumer load using one month energy data with 8928 data points. The results show that NARX- DE -GA outperformed the NARX network. The training mean square error (MSE) value for NARX- DE -GA is 0.0253 while validation is 0.0612. These values are slightly higher when compared with NARX network in previous study which are 0.0225 and 0.0533 respectively. However, the network structure which is one input and output tapped delay, and one hidden neuron is simple and applicable in real time
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    New consumer load prototype for electricity theft monitoring.
    (Published by Institute of Physics (IOP), 2013) Abdullateef, A. I; Salami, M.J.E; Musse, M.A; Onasanya, M.A; Alebiosu, M.I
    Illegal connection which is direct connection to the distribution feeder and tampering of energy meter has been identified as a major process through which nefarious consumers steal electricity on low voltage distribution system. This has contributed enormously to the revenue losses incurred by the power and energy providers. A Consumer Load Prototype (CLP) is constructed and proposed in this study in order to understand the best possible pattern through which the stealing process is effected in real life power consumption. The construction of consumer load prototype will facilitate real time simulation and data collection for the monitoring and detection of electricity theft on low voltage distribution system. The prototype involves electrical design and construction of consumer loads with application of various standard regulations from Institution of Engineering and Technology (IET), formerly known as Institution of Electrical Engineers (IEE). LABVIEW platform was used for data acquisition and the data shows a good representation of the connected loads. The prototype will assist researchers and power utilities, currently facing challenges in getting real time data for the study and monitoring of electricity theft. The simulation of electricity theft in real time is one of the contributions of this prototype. Similarly, the power and energy community including students will appreciate the practical approach which the prototype provides for real time information rather than software simulation which has hitherto been used in the study of electricity theft.
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    Parametric Model Based Approach for Consumer Load Prediction.
    (2019) Abdullateef, A. I; Akorede, M.F; Abdulkarim, A; Salami, M.J.E
    Various load prediction techniques have been proposed to predict consumer load which represents the activities of the consumer on the distribution network. Usually, these techniques use cumulative energy consumption data of the consumers connected to the power network to predict consumer load. However, these data fail to reveal and monitor the activities of individual consumer represented by the consumer load consumption pattern. A new approach of predicting individual consumer load based on autoregressive moving average model (ARMA) is proposed in this study. Sub- optimal technique of parameter estimation based on Prony method was used to determine the model order of the ARMA models ARMA (10, 8), ARMA (8, 6) and ARMA (6, 4). ARMA (6, 4) was found to be appropriate for consumer load prediction with an average mean square error of 0.00006986 and 0.0000685 for weekday and weekend loads respectively. The energy consumption data acquired from consumer load prototype for one week, with 288 data points per day used in our previous work, was used and 5-minute step ahead load prediction is achieved. Furthermore, a comparison between autoregressive AR (20) and ARMA (6, 4) was carried out and ARMA (6, 4) was found to be appropriate for consumer load prediction. This facilitates the monitoring of individual consumer activities connected on the power network