Process Modelling and Analysis Lab
- Constructing Optimal Prediction Intervals For Load Forecasting Problem
- Simulation Modelling of Pedestrian Way-finding Behaviour under Normal Situation
- Uncertainty Quantification through Construction of Prediction Intervals
- Developing Artificial Intelligent-based Metamodels for Complex Systems
Constructing Optimal Prediction Intervals for Load Forecasting Problem
Short Term Load Forecasting (STLF) is fundamental for the reliable and efficient operation of power systems. Application of artificial intelligent-based techniques and in particular Neural Networks (NNs) has proliferated for STLF within the last two decades. Despite this, one should notice that NN models are deterministic, and by that, their application for predicting future of stochastic systems, such as loads, is always in doubt and questionable.
The objective of this research is to construct Prediction Intervals (PIs) for future loads instead of forecasting their exact values. Different techniques are applied for constructing reliable PIs for outcomes of NNs. Statistical measures are developed and applied for quantitative and comprehensive evaluation of PIs. According to these measures, a new cost function is designed for shortening width of PIs without compromising their coverage probability. Evolutionary optimisation techniques are used for minimisation of this cost function and adjustment of NN parameters. Demonstrated results clearly show that the proposed method for constructing PIs outperforms the traditional techniques.
PIs constructed for test samples using the delta method (top) and optimized delta method (bottom)
Simulation Modelling of Pedestrian Way-finding Behaviour under Normal Situation
Our aim is to develop a comprehensive conceptual model of pedestrian way-finding behaviour under normal and non-panic conditions. To gain a deeper insight, it is necessary to investigate the requirements for inclusion of behavioural theories and knowledge from human dynamics, cognitive science and psychology. Research is investigating how to merge the strength of the two most plausible pedestrian modelling paradigms, Social Force Model and Discrete Choice Model with the computational efficiency of discrete event simulation. Our model consists of three main elements: environment representation, agent characteristics and behavioural rules. Initially, a 2D space with walls, one obstacle, an attraction, entrance and exit has been designed. Two entities with similar characteristics have been introduced. Currently, speed of the entities is constant and the position is manipulated by attractive and repulsive forces. Attractive force is applied around attractive spot in the environment and also another attractive force is used to motivate the entities to move to reach the target as well as repulsive force, which is for obstacle avoidance. In this study, so far the early stages of modelling have been developed and trajectories of entities are captured.
Movement trajectory of two pedestrians with different motivations while moving towards a destination
Uncertainty Quantification through Construction of Prediction Intervals
As universal approximators, Neural Networks (NNs) have achieved great success in many regression problems in the last two decades. No matter how NNs are trained or used, they suffer from two basic deficiencies: susceptible to the uncertainty, and, no indication of their accuracy. To cope with these deficiencies, construction of Prediction Intervals (PIs) for NN outputs has been proposed in literature. Although NN-based PIs have been investigated in literature for almost two decades, there are many issues left unarticulated related to them: (i) PI assessment, (ii) PI optimization, and (iii) reducing the computational requirements.
Motivated by these gaps in literature, this research attempts to develop practically useful measures for quantitative evaluation of PIs. Secondly, a new cost function is developed based on these measures and is minimised in order to find the optimum values of some critical parameters of NN models. The optimisation algorithm attempts to develop NNs leading to narrower PIs without compromising their coverage probability. Finally, a new method is proposed for PI construction, which does not require calculation of the large matrices, as they are required by other methods such as the delta and Bayesian techniques.
Terminology and concept of prediction interval
Prediction intervals for targets
Developing Artificial Intelligent-based Metamodels for Complex Systems
Motivations for this research are the recent trends in both academia and industry towards integration of simulation modelling techniques in system design and operation. Development of detailed 3D discrete event simulation models has become the common practice for modelling and analysis of complex systems. These models are, however, expert-intensive throughout their lifecycle. Besides, their computational requirement is huge, hindering their application for real time operational planning and optimization.
This research aims at developing abstract metamodels for modelling operations within complex man-made systems. A metamodel is a tool for analysis of a detailed simulation model, which provides insight into some aspect of the underlying system. Artificial intelligent based methods are used in this research for predicting the performance measures for manufacturing enterprises. Feedforward neural networks and adaptive neuro-fuzzy inference system metamodels are compared based on their performance for finding highly nonlinear relationships between independent and dependent variables. Demonstrated results indicate that both methods are capable of generating accurate point predictions. While neural network point predictions are more accurate, the neuro fuzzy models are more transparent.
From real systems to neural network metamodels
