An Accurate forecast of PV output power is essential to optimize the relationship between energy ... more An Accurate forecast of PV output power is essential to optimize the relationship between energy supply and demand. However, it is a challenging task due to the intermittent nature of solar PV output and the effect of number of meteorological variables on it. In this paper, a multivariate neural network (NN) ensemble forecast framework is proposed. First multiple neural predictors are trained with input data from meteorological variables and then accurate predictors are combined with a Bayesian model averaging (BMA) technique. To identify the best performing framework, three different NN ensemble networks are created, namely feedforward neural network (FNN), Elman backpropagation network (ELM) and cascade-forward backpropagation (NewCF) network and trained with three different training techniques. The real time recorded solar PV data along with meteorological variables of the University of Queensland's solar facility from 2014 to 2015 is used. To validate the forecast framework, one day ahead (24 h) forecasts are selected for different seasons. The results show that the proposed ensemble framework substantially improves the forecast accuracy of PV power output as compared benchmark methods, particularly for short term forecasting horizons.
• A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five differen... more • A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five different predictors along with their wavelet transformed are combined. • Bayesian model averaging technique is used to aggregate the multiple predictors. • Forecast framework is analyzed for multiple forecast horizons and buildings. • Significant error reduction in different test case studies using the framework. A B S T R A C T Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.
Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (... more Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.
Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuato... more Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities Muhammad Usman Jamil Waree Kongprawechnon Muhammad Qamar Raza Article information:
Load forecasting is very essential for efficient and reliable operation of the power system. Unce... more Load forecasting is very essential for efficient and reliable operation of the power system. Uncertainties of weather behavior significantly affect the prediction accuracy, which increases the operational cost. In this study, neural network (NN) based 168 hours ahead short term load forecast (STLF) model is proposed to study seasonal impact of calendar year. The affect of the model inputs such as, weather variables, calendar events and type of a day on load demand is considered to enhance the forecast accuracy. The weight update equations of gradient descent algorithm are derived and Mean Absolute Percentage Error (MAPE) is used as performance index. The performance of NN is measured in terms of confidence interval, which is based on training, testing, validation and cumulative impact of these phases. The simulations result shows that the forecast accuracy is affected by seasonal variation of input data.
Accurate load forecasting has always been an important issue for efficient and reliable operation... more Accurate load forecasting has always been an important issue for efficient and reliable operation of the power system. Load forecasting shows non linear characteristics due to several influencing factors. In this paper, comparison of Back Propagation (BP) and Levenberg Marquardt (LM) neural network (NN) based forecast model is presented for seasonal prediction. The historical load and weather data of four year is used as input of forecast model. To enhance the forecast accuracy of model, type and hour of day, day of week, dew point and correlated historical load data is treated as input of model. The forecast model is used to predict the 168 hours ahead load demand of winter, spring, summer and autumn season. The mean absolute percentage errors (MAPE), Convergence and regression analysis of NN training are used to measure the NN performance index. To avoid the over fitting problem in NN training process, the load data sets is divided into training and validation data sets. The real time load and weather data of power grid is used to validate the model. LM based forecast model outperforms than the BP model in terms of forecast accuracy, convergence rate and training of the network.
Accurate load forecasting has always been an important issue for efficient and reliable operation... more Accurate load forecasting has always been an important issue for efficient and reliable operation of the power system. Load forecasting shows non linear characteristics due to several influencing factors. In this paper, comparison of Back Propagation (BP) and Levenberg Marquardt (LM) neural network (NN) based forecast model is presented for seasonal prediction. The historical load and weather data of four year is used as input of forecast model. To enhance the forecast accuracy of model, type and hour of day, day of week, dew point and correlated historical load data is treated as input of model. The forecast model is used to predict the 168 hours ahead load demand of winter, spring, summer and autumn season. The mean absolute percentage errors (MAPE), Convergence and regression analysis of NN training are used to measure the NN performance index. To avoid the over fitting problem in NN training process, the load data sets is divided into training and validation data sets. The real time load and weather data of power grid is used to validate the model. LM based forecast model outperforms than the BP model in terms of forecast accuracy, convergence rate and training of the network.
Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (... more Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.
Short-term load forecast plays an important role in planning and operation of power systems. The ... more Short-term load forecast plays an important role in planning and operation of power systems. The accuracy of the forecast value is necessary for economically efficient operation and effective control of the plant. This study describes the methods of Autoregressive (AR) Burg's and Modified Covariance (MCOV) in solving the short term load forecast. Both algorithms are tested with power load data from Malaysian grid and New South Wales, Australia. The forecast accuracy is assessed in terms of their errors. For the comparison the algorithms are tested and benchmark with the previous successful proposed methods.
Load forecasting is very essential for efficient and reliable operation of the power system. Unce... more Load forecasting is very essential for efficient and reliable operation of the power system. Uncertainties of weather behavior significantly affect the prediction accuracy, which increases the operational cost. In this study, neural network (NN) based 168 hours ahead short term load forecast (STLF) model is proposed to study seasonal impact of calendar year. The affect of the model inputs such as, weather variables, calendar events and type of a day on load demand is considered to enhance the forecast accuracy. The weight update equations of gradient descent algorithm are derived and Mean Absolute Percentage Error (MAPE) is used as performance index. The performance of NN is measured in terms of confidence interval, which is based on training, testing, validation and cumulative impact of these phases. The simulations result shows that the forecast accuracy is affected by seasonal variation of input data.
Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuato... more Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities Muhammad Usman Jamil Waree Kongprawechnon Muhammad Qamar Raza Article information:
An Accurate forecast of PV output power is essential to optimize the relationship between energy ... more An Accurate forecast of PV output power is essential to optimize the relationship between energy supply and demand. However, it is a challenging task due to the intermittent nature of solar PV output and the effect of number of meteorological variables on it. In this paper, a multivariate neural network (NN) ensemble forecast framework is proposed. First multiple neural predictors are trained with input data from meteorological variables and then accurate predictors are combined with a Bayesian model averaging (BMA) technique. To identify the best performing framework, three different NN ensemble networks are created, namely feedforward neural network (FNN), Elman backpropagation network (ELM) and cascade-forward backpropagation (NewCF) network and trained with three different training techniques. The real time recorded solar PV data along with meteorological variables of the University of Queensland's solar facility from 2014 to 2015 is used. To validate the forecast framework, one day ahead (24 h) forecasts are selected for different seasons. The results show that the proposed ensemble framework substantially improves the forecast accuracy of PV power output as compared benchmark methods, particularly for short term forecasting horizons.
• A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five differen... more • A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five different predictors along with their wavelet transformed are combined. • Bayesian model averaging technique is used to aggregate the multiple predictors. • Forecast framework is analyzed for multiple forecast horizons and buildings. • Significant error reduction in different test case studies using the framework. A B S T R A C T Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.
An Accurate forecast of PV output power is essential to optimize the relationship between energy ... more An Accurate forecast of PV output power is essential to optimize the relationship between energy supply and demand. However, it is a challenging task due to the intermittent nature of solar PV output and the effect of number of meteorological variables on it. In this paper, a multivariate neural network (NN) ensemble forecast framework is proposed. First multiple neural predictors are trained with input data from meteorological variables and then accurate predictors are combined with a Bayesian model averaging (BMA) technique. To identify the best performing framework, three different NN ensemble networks are created, namely feedforward neural network (FNN), Elman backpropagation network (ELM) and cascade-forward backpropagation (NewCF) network and trained with three different training techniques. The real time recorded solar PV data along with meteorological variables of the University of Queensland's solar facility from 2014 to 2015 is used. To validate the forecast framework, one day ahead (24 h) forecasts are selected for different seasons. The results show that the proposed ensemble framework substantially improves the forecast accuracy of PV power output as compared benchmark methods, particularly for short term forecasting horizons.
• A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five differen... more • A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five different predictors along with their wavelet transformed are combined. • Bayesian model averaging technique is used to aggregate the multiple predictors. • Forecast framework is analyzed for multiple forecast horizons and buildings. • Significant error reduction in different test case studies using the framework. A B S T R A C T Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.
Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (... more Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.
Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuato... more Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities Muhammad Usman Jamil Waree Kongprawechnon Muhammad Qamar Raza Article information:
Load forecasting is very essential for efficient and reliable operation of the power system. Unce... more Load forecasting is very essential for efficient and reliable operation of the power system. Uncertainties of weather behavior significantly affect the prediction accuracy, which increases the operational cost. In this study, neural network (NN) based 168 hours ahead short term load forecast (STLF) model is proposed to study seasonal impact of calendar year. The affect of the model inputs such as, weather variables, calendar events and type of a day on load demand is considered to enhance the forecast accuracy. The weight update equations of gradient descent algorithm are derived and Mean Absolute Percentage Error (MAPE) is used as performance index. The performance of NN is measured in terms of confidence interval, which is based on training, testing, validation and cumulative impact of these phases. The simulations result shows that the forecast accuracy is affected by seasonal variation of input data.
Accurate load forecasting has always been an important issue for efficient and reliable operation... more Accurate load forecasting has always been an important issue for efficient and reliable operation of the power system. Load forecasting shows non linear characteristics due to several influencing factors. In this paper, comparison of Back Propagation (BP) and Levenberg Marquardt (LM) neural network (NN) based forecast model is presented for seasonal prediction. The historical load and weather data of four year is used as input of forecast model. To enhance the forecast accuracy of model, type and hour of day, day of week, dew point and correlated historical load data is treated as input of model. The forecast model is used to predict the 168 hours ahead load demand of winter, spring, summer and autumn season. The mean absolute percentage errors (MAPE), Convergence and regression analysis of NN training are used to measure the NN performance index. To avoid the over fitting problem in NN training process, the load data sets is divided into training and validation data sets. The real time load and weather data of power grid is used to validate the model. LM based forecast model outperforms than the BP model in terms of forecast accuracy, convergence rate and training of the network.
Accurate load forecasting has always been an important issue for efficient and reliable operation... more Accurate load forecasting has always been an important issue for efficient and reliable operation of the power system. Load forecasting shows non linear characteristics due to several influencing factors. In this paper, comparison of Back Propagation (BP) and Levenberg Marquardt (LM) neural network (NN) based forecast model is presented for seasonal prediction. The historical load and weather data of four year is used as input of forecast model. To enhance the forecast accuracy of model, type and hour of day, day of week, dew point and correlated historical load data is treated as input of model. The forecast model is used to predict the 168 hours ahead load demand of winter, spring, summer and autumn season. The mean absolute percentage errors (MAPE), Convergence and regression analysis of NN training are used to measure the NN performance index. To avoid the over fitting problem in NN training process, the load data sets is divided into training and validation data sets. The real time load and weather data of power grid is used to validate the model. LM based forecast model outperforms than the BP model in terms of forecast accuracy, convergence rate and training of the network.
Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (... more Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.
Short-term load forecast plays an important role in planning and operation of power systems. The ... more Short-term load forecast plays an important role in planning and operation of power systems. The accuracy of the forecast value is necessary for economically efficient operation and effective control of the plant. This study describes the methods of Autoregressive (AR) Burg's and Modified Covariance (MCOV) in solving the short term load forecast. Both algorithms are tested with power load data from Malaysian grid and New South Wales, Australia. The forecast accuracy is assessed in terms of their errors. For the comparison the algorithms are tested and benchmark with the previous successful proposed methods.
Load forecasting is very essential for efficient and reliable operation of the power system. Unce... more Load forecasting is very essential for efficient and reliable operation of the power system. Uncertainties of weather behavior significantly affect the prediction accuracy, which increases the operational cost. In this study, neural network (NN) based 168 hours ahead short term load forecast (STLF) model is proposed to study seasonal impact of calendar year. The affect of the model inputs such as, weather variables, calendar events and type of a day on load demand is considered to enhance the forecast accuracy. The weight update equations of gradient descent algorithm are derived and Mean Absolute Percentage Error (MAPE) is used as performance index. The performance of NN is measured in terms of confidence interval, which is based on training, testing, validation and cumulative impact of these phases. The simulations result shows that the forecast accuracy is affected by seasonal variation of input data.
Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuato... more Adaptive neural network based backstepping control design for MIMO nonlinear systems with actuator nonlinearities Muhammad Usman Jamil Waree Kongprawechnon Muhammad Qamar Raza Article information:
An Accurate forecast of PV output power is essential to optimize the relationship between energy ... more An Accurate forecast of PV output power is essential to optimize the relationship between energy supply and demand. However, it is a challenging task due to the intermittent nature of solar PV output and the effect of number of meteorological variables on it. In this paper, a multivariate neural network (NN) ensemble forecast framework is proposed. First multiple neural predictors are trained with input data from meteorological variables and then accurate predictors are combined with a Bayesian model averaging (BMA) technique. To identify the best performing framework, three different NN ensemble networks are created, namely feedforward neural network (FNN), Elman backpropagation network (ELM) and cascade-forward backpropagation (NewCF) network and trained with three different training techniques. The real time recorded solar PV data along with meteorological variables of the University of Queensland's solar facility from 2014 to 2015 is used. To validate the forecast framework, one day ahead (24 h) forecasts are selected for different seasons. The results show that the proposed ensemble framework substantially improves the forecast accuracy of PV power output as compared benchmark methods, particularly for short term forecasting horizons.
• A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five differen... more • A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five different predictors along with their wavelet transformed are combined. • Bayesian model averaging technique is used to aggregate the multiple predictors. • Forecast framework is analyzed for multiple forecast horizons and buildings. • Significant error reduction in different test case studies using the framework. A B S T R A C T Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.
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