Development of an Improved Short-Term Peak Load Forecasting Model Based on Seasonal Autoregressive Integrated Moving Average and Nonlinear Autoregressive Neural Network for Nigeria Power System Grid.
ABSTRACT
demand is a central and process for planning operations and facility expansion in the electricity . The demand pattern is very complex due to the highly unpredictable behavior of consumers’ load consumption.
Therefore, finding an appropriate forecasting model for a specific electricity network at peak demand is not an easy task for utilities and policymakers. Many load forecasting methods developed in the past decades were characterized by poor precision, and large forecast error because of their inability to adapt to changes in dynamics of load demand.
To fill this gap, this research has developed an improved short-term daily peak load forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Nonlinear Autoregressive Neural Network (NARX).
The developed model used SARIMA to captures the linear pattern (trend) and seasonality of the load time series but due to the seasonal and cyclical nature of the load behavior which cannot accurately describe by the linear regression model. NARX neural network was combined with SARIMA in order to improve and captures the non-linear patterns of the data series to minimize its forecast error.
The structures of NARX were optimized by the tenets of chaos theory to avoid trial by error approach during training.
A daily peak load data of Nigeria power system grid and daily average weather data for ten years, from January 1st, 2006 to December 31st, 2015 were used in this study to complete the short-term load forecasting using MATLAB 2015a environment for simulation and mean absolute percentage error (MAPE) as a measure of accuracy.
The model forecast result was validated and compared with real peak load demand data of the Nigeria grid in 2015 to measure the performance of the method.
The evaluation results showed that the developed model trained with the Levenberg-Marquardt training algorithm (LM) is more effective and performs better than the classical SARIMA model with MAPE of 2.41%, correlation coefficient of 96.59% which is equivalent to an improvement of 63.70% in error reduction.
Performance of different training methods also compare on the developed method and results show that developed model training with LMshows more superiority and high precision over Bayesian regularization training algorithm (Br) with 1.6318% in error reduction equivalent to an improvement of 40.37%.
Finally, the proposed model has been further been used to forecasts the daily peak load demand of years 2017 and 2018 successfully for planning and operations of the grid.
TABLE OF CONTENT
Page number
Cover Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgment v
Abstract vii
List of Figures xii
List of Plates xv List of Tables xvi
List of Abbreviation xvii
CHAPTER ONE: INTRODUCTION
1.1 Background of study 1
1.2 Motivation 5
1.3 Significance of Research 6
1.4 Statement of Research Problem 6
1.5 Aim and Objectives 7
1.6 Methodology 8
1.7 Dissertation Organization 10
CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction 11
2.2 Review of Fundamental Concepts 11
2.2.1 Electrical Power System Planning & Operations 11
2.2.2 Application and Need for Power System Planning 12
2.2.3 Electric Power Peak demand 12
2.2.4 Factors Affecting the Load Profile Patterns 13
2.2.5 Short-term Load Forecasting 14
2.2.6 Nonlinear Dynamics and Chaos and Its Application in Electric Power System 16
2.3 Autoregressive Integrated Moving Average (ARIMA) Modeling 24
2.3.1 Seasonal Autoregressive Integrated Moving Average Model (SARIMA) 26
2.4 Artificial Neural Network (ANN) 28
2.4.1 The Multilayer Perceptron (MLP) Neural Network 30
2.4.2 Nonlinear Autoregressive neural network with exogenous inputs (NARX) 34
2.4.3 How to Build a NARX Neural Network 36
2.4.4 Concatenation of Seasonal ARIMA and NARX Model 42
2.4.5 Cubic spline interpolation 43
2.4.6 Correlation coefficient 44
2.5 Review of Existing Similar Works 45
CHAPTER THREE: MATERIALS AND METHOD
3.1 Introduction 55
3.2 Materials and Equipment Required for the study 55
3.2.1 Materials Required 55
3.2.2 Equipment Required 55
3.2.3 Data Sources 56
3.3 The Study Area 56
3.4 Data Pre-processing 56
3.5 Methodology 59
3.5.1 Characterization of Peak Load Data Using Nonlinear Dynamic Analysis
Approach 59
3.5.2 Development of an Improved SARIMA Based on NARX Neural Network Optimized with Chaos Theory Approach for Days Ahead Prediction 61
3.5.3 Evaluation of the Proposed Model Forecast Performance 69
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 Introduction 72
4.2 Results of the Characterization of Peak Load Data Using Nonlinear Analysis 72
4.2.1 The Data Descriptive Analysis Results 72
4.2.2 Results of the Analysis of the Peak Load Demand Data using Time series
plot and power spectrum (visualization of variables) 73
4.2.3 Result of Phase Portrait Analysis 77
4.2.4 Results of the Phase space reconstruction and computation of Lyapunov
exponent 78
4.3 Simulation Results for Proposed Improve SARIMA Based NARX Network Model 82
4.3.1 Simulation Results for Multiplicative SARIMA Model 83
4.3.2 Simulation Results for Proposed NARX Network for Daily Peak Load Demand
of Nigeria Power System Grid 89
4.3.3 Comparison of the Models Performance Evaluation Results 97
4.4 Results of Forecasting the Daily Peak Load Demand for the Year 2017 and 2018
Using the Proposed Model 100
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION
5.1 Conclusion 102
5.2 Significant Contribution 103
5.3 Limitation 103
5.4 Recommendations for Future Research 104
REFERENCES 105
APPENDICES 112
INTRODUCTION
1.1 Background of the Study
The growing concern of rapid urbanization globally has presented the world with tasks of meeting the daily increase in electricity demand and transaction in the past few decades (Al-Kandari &Solima 2005).
But meeting this demand is still a major concern to many of the electric power utility companies globally because large electric energy produced cannot be stored; therefore, it must be consumed at the same time as it is being produced (Musa, 2017; Luiz et al., 2015).
To tackle this challenge of inadequate power generation and supply in many nations lead to the deregulation of the electric power industry. In Nigeria, the power sector was deregulated into three entities: generation, transmission, and distribution companies, which further unbundle into 18 successor companies (Nwohu, 2009).
Nonetheless, the operation of these utility companies in the last decade has not really improved the situation of the unavailability of electricity in the country. This may be attributed to poor planning and inadequate infrastructure facilities for the generation, transmission, and distribution of power to end-users.
On the other hand, in the planning process pre-information about the future energy needs is the key strategies to guarantee efficiency which is achieved through forecasting. However, many utilities nowadays are still faced with challenges of how to accurately forecast their load’s requirement at various times.
The foremost reason behind this task is that power demand in any locality or region and nationwide vary with growth in population and economic activities (Popoola & Ibrahim 2014). This has made accurate load forecasting very critical for a day–to–day planning and operation of the power grid system (Musa et al., 2014).
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