Traditionally, the success of a solution can only be determined after its implementa-tion or deployment. In real world soluimplementa-tion deployments, regardless of size, financial investments are considered well spent if the goals are met. The measures of suc-cess are found in the data collected over time when the deployment is in operation.
Hence, this study seeks to meet the following objectives:
• Determine the model’s success factors
• Determine the method for data aggregation
• Generate the artificial model
• Generalize the created model
First, we develop a systematic approach to study the scenario of a non-existent de-ployed system consistently. Then, we identify the constraints and processes that best represent the scenario. Next, we identify tools that can synthesize data that repre-sents the scenario. Finally, we generalize the processes to create a framework that can help manage resources. Each of these four objectives has its own challenges.
The first challenge presented here is how to determine success without real-world deployment. With reference to EVs and elevators, the first challenge frames how we determine the amount of energy required to power a nation’s electrified transportation system and elevators in all buildings with mixed functions before they are built.
To determine success, data is needed to justify the result. Since there is no real-world deployment, the second challenge is how to obtain the data. There exist many ways to synthesize data for the above purpose. However, the data need to have properties similar to the real-world data. The synthetic data need to include the effects of causal relationships, similar to those found in the real world. Some of them may be complicated, which means an initial action may lead to a chain of cause and effects that are link to one another consecutively. Hence, we need to closely model the required simulation to the real-world situation. In this study, we need to map EVs on a road network system and elevator systems in a building sep-arately but accounting for possible daily variations in both cases.
In creating data that meet the above requirements, the third challenge is to search for a model capable of performing this function. Empirical methods employ mea-surements and observations made under different conditions to build models. How-ever, due to the lack of real-world deployment, empirical evidence is unavailable.
Statistical methods translate problems into constraints and equations to create mod-els. However, it is difficult to define all equations to include all real world possibili-ties that will characterize the data. We need to find a data creation model capable of
taking in constraints without losing flexibility to account for uncertainties in human behavior.
The fourth challenge is to generalize the model so that it can be applied to simi-lar problem statements. There is also a need to find relevant real-world information to support the generalized model. Hence, we need to consider the commonalities in the steps used in each case study and set them in a serial chain of processes.
The remaining thesis is structured as follows. In Chapter 2, we illustrate the conceptual framework adopted to optimize the efficient use of resources. It does so by explaining the sequential steps of modeling a research problem with real-world details, assumptions and constraints and executing a simulation. The final step in achieving the optimized outcome is through forecasting with data generated from the simulation.
In Chapter 3, we examine three methods of forecasting and discuss the approach used to select the most appropriate method to achieve the required outcome. In Chapter 4, we illustrate applicable areas where benefits can be obtained when ap-plying the methodology formulated herein. In Chapter 5, we summarize the au-thor’s contributions to this study. The thesis concludes with Chapter 6, in which we suggest future avenues of research.
2 OPTIMIZATION
Figure 2.1:Optimization framework used in this study.
The most effective way to optimize the required resources for a system is to know the precise requirements in advance. This is trivial for a system that has processes consuming constant resources in a repeating pattern. However, it becomes difficult when the processes’ consumption start to vary, as the necessary data are not readily available. In light of resource depletion, there is a strong desire to optimize systems for reducing resource wastage.
Fig. 2.1 shows the framework used for optimization in this study. To optimize systems, two important elements must be available: historical data that capture trends intrinsic to the system (’Data’ in Fig. 2.1) and forecast methods capable of accurately predicting future data (’Insights’ in Fig. 2.1). For the former, if the system does not exist in real world, we will need to synthesize the relevant data (’Problem’
and ’Modeling’ in Fig. 2.1). For the latter, simple methods (e.g., linear regression) and more complex ones (e.g., neural networks, support vector machines) exist for forecasting.
2.1 SYNTHETIC DATA
Synthetic data are acceptable in the absence of actual data, as it has similar attributes, relationships, and other factors. There are many ways to synthesize data. However, to solve real-world problems, synthesized data need to contain the same flexibility
and unpredictability as actual data.
Many ways exist to synthesize data. Classical approaches are either empiri-cal [15, 16] or stochastic [17, 18], as shown in other fields, because massive energy consumption measurements for EVs do not exist yet. Empirical approaches involve structural and well-defined processes that produce verifiable observations and evi-dence. They are not based on pure theory or logic. They are measured using physi-cal means, such as measuring temperature with a thermometer, length with a ruler, or, as in paperIV, counting human traffic with sensors. The evidence produced by these approaches must be valid and reliable. Validity means the investigation is ap-plicable to the problem statement or query. Reliability means similar observations are reproducible under the same conditions [15, 16]. However, an existing physical model must first be in place so that measurements can be taken to build the empir-ical model.
As previously stated, the physical model needs to exist for taking measurements to build a data-synthesizing empirical model. Currently, most cars are not built to log travel data for each journey. Some cars may have additional sensors to log such data, but they are not representative of the car population. Although event data recorders exists, they do not capture vehicle data under normal operations on a long-term basis [19]. Even if data loggers could be easily installed, considerable effort would still be required to enlist cars to participate in this event-capturing re-search. Hence, an empirical model is not realistically feasible at this time.
Stochastic approaches involve random processes that produce observations based on probability. They describe systems that change in undefinable ways, such as where the ball will land on a roulette wheel or the hourly total residential en-ergy load of a given community. In these examples, the result space is bound, but how each observation is made cannot be determined. Each observation is inde-pendent [17, 18]. Hence, in artificial intelligence,simulated annealing[20, 21],neural networks[22,23] andgenetic algorithms[24,25] which involves the use of probabilities to solve problems are considered stochastic methods.
Since logging journey information can be difficult, many researchers use stochas-tic methods to model and perform data traffic flow analysis [26–28] because no real-world deployment is required to acquire similar related data. As actual massive deployment of data logging sensors can be exorbitant and time-consuming, stochas-tic methods are well suited for modeling road traffic.
However, empirical approaches cannot be used to generate energy consumption data for traveling EVs because road network systems capable of generating the data have not been built. Hence, stochastic approaches, which involve modeling the sys-tem and estimating its outputs, are currently the only available option. The method chosen to synthesize the data depends on the modeled situation.
In papers II and V, Monte Carlo [29, 30] and Poisson [31, 32] distributions are used, respectively, in conjunction with multi-agent systems to synthesis the data re-quired for model analysis. A Monte Carlo distribution process is a computerized mathematical method that augments analysis and decision-making by accounting for uncertainty. It considers a set of possible outcomes and simulates a likelihood
table for each of them by re-calculating their likelihoods over many trials. Using a Monte Carlo distribution is advantageous because it shows not only the possible outcomes but also the likelihood of each outcome. It is also able to show the rela-tionship between inputs and outcomes [30]. It was used in paperIIto model the power-brokers’ participation in Singapore’s energy market using a newly introduced curtailment program as an energy brokering instrument. Monte Carlo distributions were used to synthesize users’ likelihood of successfully curtailing their energy ex-penses when required by the curtailment program.
A Poisson distribution describes the likelihoods for a set of independent occur-rences at fixed intervals in a discrete probability distribution [31]. In paperV, a Poisson distribution was suitable for synthesizing the number of passengers arriv-ing at the lift lobby on the various floors of a buildarriv-ing. The number of passengers per arrival is independent, which fits the description of how a Poisson should be used [32]. In paperVthe distribution accounted for the types of businesses on each floor, as it affects the various peak lift-usage times. For example, buildings with product service centers operating during the lunch hour may have more lift activity than a building without such businesses
More complex methods have been used in modeling for data synthesis. Artifical neural networkshave been used to model steel structures and create data for reli-ability analysis [33]. Genetic algorithms have been used to create wind conditions data for the optimization of wind turbine design [34]. However, in this study, we are modeling human behavior and need a method that does not make random and arbitrary decisions. Furthermore, we must be able to account for uncertainty and make deterministic adaptive changes during the simulation. Multi-agent systems meet the requirements of this study, and we will use them to explore and perform data synthesis.