Review on Construction Procedures of Driving Cycles

The goal of this paper is to give an overview of the literature of construction techniques of driving cycles. Our motivation for the overview is the future goal of constructing our own driving cycles for various types of vehicles and routes. This activity is part of a larger project focusing on determination of fuel and energy consumption by dynamic simulation of vehicles. Accordingly, the papers dealing with sample route determination, data collection and processing, driving cycle construction procedures, statistical evaluation of data are in our focus

A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information

Surface ozone (O3) is considered an hazard to human health, affecting vegetation crops and ecosystems. Accurate time and location O3 forecasting can help to protect citizens to unhealthy exposures when high levels are expected. Usually, forecasting models use numerous O3 precursors as predictors, limiting the reproducibility of these models to the availability of such information from data providers. This study introduces a 24 h-ahead hourly O3 concentrations forecasting methodology based on bagging and ensemble learning, using just two predictors with lagged O3 concentrations. This methodology was applied on ten-year time series (2006–2015) from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to its forecasting performance and wide applicability, but also as benchmark methodology

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks

Air Quality and Source Apportionment

Atmospheric particulate matter (PM) is known to have far-ranging impacts on human health through to climate forcing. The characterization of emission sources and the quantification of specific source impacts to PM concentrations significantly enhance our understanding of, and our ability to, eventually predicting the fate and transport of atmospheric PM and its associated impacts on humans and the environment. Recent advances in source apportionment applications have contributed unique combinations of chemical and numerical techniques for determining the contributions of specific sources, including diesel exhaust and biomass burning. These advances also identify and help characterize the contributions of previously uncharacterized sources. Numerical modeling has also enabled estimations of contributions of emission sources to atmospherically processed PM in urban and rural regions. Investigation into the emissions sources driving air quality is currently of concern across the globe. This Special Issue offers studies at the intersection of air quality and source apportionment for study areas in China, Germany, Iceland, Mexico, and the United States. Studies cover diverse methods for chemical characterization and modeling of the impact of different emission sources on air quality

A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems, 201

Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

Air Pollution Control and Sustainable Development

This book brings together the latest research findings on the state of air pollution control and its impact on economic growth in different countries. The book has substantial content and rich discussion. It is suitable for students and researchers at different levels to learn the status of air pollution, governance policies and their effects, and the relationship between pollution control and economic growth in countries around the world

Application of machine learning and deep learning methods for load prediction in institutional buildings

Worldwide, the building sector consumes a significant amount of energy in different stages such as construction and operation. Depending on the type of energy source used, buildings have a considerable impact on air pollution and greenhouse gas emissions. To reduce the amount of emissions from the building sector and manage energy consumption, many tools and incentives are used around the world. One of the most recent and successful approaches in this regard is the application of machine learning techniques in building engineering. The increasing availability of real-time data measured by sensors and building automation systems enable the owner and energy planner to analyze the collected information and explore the hidden useful knowledge and use it to answer specific questions such as which parts need retrofit, how much energy can be saved and what would be the cost. At the building level, machine learning has different applications, such as pattern extraction and load prediction. Amongst those, load analysis and energy demand prediction are of specific importance for the building energy managers, as it can lead to a more efficient operation schedule of energy systems in the building. The analysis of load profiles can give a good overview of the energy use and user behavior in the building. Detailed load analysis and understanding is an essential step before the predictive analysis. In this study, electrical load data from three transformers installed in EV building, Concordia University and weather data collected from the weather station installed in EV building were used for load analysis and load prediction. EV building includes two main parts, which are Engineering (ENCS) and visual arts departments (VA). The three transformers considered in this study measure heating, ventilation, and air conditioning (HVAC) load from a mechanical room (located in 17th floor of the EV building) in addition to the plug and miscellaneous loads from ENCS and VA departments. In the load analysis part, the representative daily loads of these three transformers of the building are studied. The magnitude and trend of daily loads are extracted and discussed. The average load from 17th floor’s transformer is found to be 1,441 kW during office hours of weekdays in summer, whereas this load during office time in winter is 991 kW. Note that, this load does not include the gas consumption, used for meeting the heating load during the winter. Regarding the plug load from ENCS and VA department, the average load during office hours of weekdays in summer is 512 kW, and 453 kW, respectively. Moreover, the load reduction during the COVID19 pandemic is studied by comparing the two months (April and May) of 2019 and 2020 for all three transformers. There was a significant reduction of 42 % for the load of 17th floor between April 2019 and April 2020 (weekdays), while 24% and 40% load reduction was observed for ENCS and VA transformers, respectively. Based on the results during COVID 19 period, we see that the existence of people in the building affects the load, but a great part of the load is related to the schedule and policy of the building. That is why there is a good potential to save energy just by changing the schedule and plans that systems are running based on. The second part of the work deals with load prediction using regression analysis and long shortterm memory (LSTM) model. The importance of input variables for load prediction is evaluated in the regression section. In linear regression, twenty scenarios are considered. Each scenario is a different combination of input features. It was found from the results that the best scenario is when all calendar and weather data are considered as input attributes. The best scenario in winter has R2=0.29 and MAPE=24.46, while in summer, R2=0.64 and MAPE=10.47. The results are confirmed with correlation analysis. For this case study, adding meteorological data did not improve prediction in winter significantly because in winter, gas is used for heating and the considered data does not reflect it, but in summer, weather variables were of great importance. Also, specific and unusual events in consumption could be detected with polynomial regression. Regarding load forecasting, LSTM is used as a deep learning model, which considers the sequential load data and predicts future load for different time horizons. Regarding the size of the dataset and LSTM parameters, the best performance was obtained for one-year ahead forecasting with R2= 0.75, and MAPE= 10.97. Another result was that the type of load influences the performance of the LSTM model. Considering different load types, the plug and lighting loads from the ENCS and VA departments could be better predicted than the 17th floor HVAC load, since HVAC load is affected by weather variables that are fluctuating and not easy to predict, but plug loads are more related to the schedule of building. The other influencing factor on prediction performance is the choice of train-set and test-set. The lowest R-squared belongs to the model that has the year 2019 as test-set. The results of this project could be useful for building facility managers to adapt and optimize the schedule of the energy systems and give recommendations to the users to improve energy efficiency

Road transport and emissions modelling in England and Wales: A machine learning modelling approach using spatial data

An expanding street network coupled with an increasing number of vehicles testifies to the significance and reliance on road transportation of modern economies. Unfortunately, the use of road transport comes with drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to countries’ objectives to improve air quality and a barrier to the ambitious targets to reduce Greenhouse Gas emissions. Unsurprisingly, traffic forecasting, its environmental impacts and potential future configurations of road transport are some of the topics which have received a great deal of attention in the literature. However, traffic forecasting and the assessment of its determinants have been commonly restricted to specific, normally urban, areas while road transport emission studies do not take into account a large part of the road network, as they usually focus on major roads. This research aimed to contribute to the field of road transportation, by firstly developing a model to accurately estimate traffic across England and Wales at a granular (i.e., street segment) level, secondly by identifying the role of factors associated with road transportation and finally, by estimating CO2 and air pollutant emissions, known to be responsible for climate change as well as negative impacts on human health and ecosystems. The thesis identifies potential emissions abatement from the adoption of novel road vehicles technologies and policy measures. This is achieved by analysing transport scenarios to assess future impacts on air quality and CO2 emissions. The thesis concludes with a comparison of my estimates for road emissions with those from DfT modelling to assess the methodological robustness of machine learning algorithms applied in this research. The traffic modelling outputs reveal traffic patterns across urban and rural areas, while traffic estimation is achieved with high accuracy for all road classes. In addition, specific socioeconomic and roadway characteristics associated with traffic across all vehicle types and road classes are identified. Finally, CO2 and air pollution hot spots as well as the impact of open spaces on pollutants emissions and air quality are explored. Potential emission reduction with the employment of new vehicle technologies and policy implementation is also assessed, so as the results can support urban planning and inform policies related to transport congestion and environmental impacts mitigation. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels