Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.
All the social, economic and industrial development depends on the availability of energy. Since energy demand is increasing exponentially throughout the world, more and more CO2 is being emitted out into the atmosphere, giving rise to global warming. Therefore, establishing a sustainable environment is becoming increasingly important. It has been found through research that domestic sector contributes a great deal to the rising energy consumption. Due to prevailing energy crisis, efforts are being made to reduce the increasing energy consumption and make efficient use of energy by making the buildings energy efficient. For this, realistic assessment of energy use patterns in existing houses and buildings is necessary to assure dataset accuracy. Living lab concept integrated with sensor technologies can be used for assessment of such patterns. This paper presents living lab concept for sensor-based energy performance assessment of Houses. First, detailed literature review to benchmark concepts of energy efficiency of buildings, living labs concept, sensor based assessment, energy audit, and application of living lab concept has been discussed. Thereafter, sensors based living lab assessment and living lab approach has been introduced as being utilized by the author in a research project for development of guidelines for energy efficient housing. The paper also highlights important parameters to be monitored that effect energy performance. The concept reflects usefulness of living lab concept for sensor-based energy performance assessment of houses that help in substantial reduction in the energy consumption. As such data can be utilized for both realistic energy simulations by improving level of development of models as well as better usage comparisons with modeled analysis, hence helping in identifying true and effective improvement measures
Some recent research in the area of light shelves has been focused on applying photovoltaic modules to light shelves to save building energy. However, due to the modules installed on the light shelf reflectors, most such light shelves have failed to improve both daylighting and generation efficiency. This study proposes a folding technology to improve light shelves’ daylighting and generation efficiency that uses photovoltaic modules and validates their performance using a testbed. The major obtained findings are as follows: (1) The proposed folding technology has a structure in which reflectors and photovoltaic modules fold alternately by modularizing the light shelf. The reflector and photovoltaic modules are controlled by adjusting the degree of folding. (2) Because light shelf angles for improving daylighting and generation differed depending on the application of the photovoltaic module, the optimal light shelf specifications differed. (3) Compared to previous light shelf technologies, the light shelf with folding technology and a photovoltaic module reduced energy use by 31.3% to 38.2%. This demonstrates the efficacy of the proposed system. (4) Applying a photovoltaic module can lower the indoor uniformity ratio, which means that the daylighting performance of the light shelf is degraded due to the reduction of the area occupied by the reflector.
Transport energy conservation research in urban transport systems dates back principally to the Organization of the Petroleum Exporting Countries’ (OPEC) “Arab Oil Embargo” (1973–1974) and the Iranian revolution (1979), when global oil supplies became threatened and costs rose steeply. Two subsequent Gulf Wars (1991 and 2003) highlighted the dangerous geo-political dimensions of Middle-Eastern oil. In latter times, the urgency to reduce global CO2 output to avoid catastrophic climate change has achieved great prominence. How to reduce passenger transport energy use therefore remains an important goal, which this paper pursues in ten Swedish cities, based on five scenarios: (1) increasing the relatively low public transport (PT) seat occupancy in each Swedish city to average European levels (buses 35%, light rail 48%, metro 60% and suburban rail 35%); (2) doubling existing PT seat occupancy in each Swedish city; (3) increasing existing car occupancy in each Swedish city by 10%; (4) decreasing existing energy use per car vehicle kilometer by 15%; (5) increasing existing modal split for daily trips by non-motorized modes to 50% in each city. A sixth “best-case scenario” is also explored by simultaneously combining scenarios 2 to 5. The data used in the paper come from systematic empirical research on each of the ten Swedish cities. When applied individually, scenario 2 is the most successful for reducing passenger transport energy use, scenarios 1 and 4 are next in magnitude and produce approximately equal energy savings, followed by scenario 5, with scenario 3 being the least successful. The best-case, combined scenario could save 1183 million liters of gasoline equivalent in the ten cities, representing almost a 60% saving over their existing 2015 total private passenger transport energy use and equivalent to the combined 2015 total annual private transport energy use of Stockholm, Malmö and Jönköping. Such findings also have important positive implications for the de-carbonization of cities. The policy implications of these findings and the strategies for increasing public transport, walking and cycling, boosting car occupancy and decreasing vehicular fuel consumption in Swedish cities are discussed.
The change and increasing diversity in communication tools with globalization have ensured the universal presentation of visual elements shaped by local cultural patterns. Today, design products have turned into a strategic tool for many countries in order to reach higher competitive power. As the visual design industry contributes as an important visual communication and marketing tool for different industries within and outside the creative sector, its role in ensuring sustainability becomes even more important. Graphic design is much more effective, direct, and fast than other means of information in order to develop environmental awareness and motivate desired behaviors. The biomimetic graphic is a design process and style that has been spread and passed along throughout today’s culture and society via various channels and networks of communication. In today's world, where globalization and associated social and environmental problems are increasing, design education plays an important role in the development of sustainable design products. In design education, the students should be taught the concepts of ecological material selection, environmentally friendly energy use, recycling, reconsideration, and reuse. In this study, the role of biomimicry in visual design education will be examined with sample applications. In addition to new skills and knowledge in design education, sustainability will emerge as an important feature sought in designers soon.
Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.
This study conducts an empirical investigation about the moderating role of the informal economy on Turkey's environmental performance by employing advanced econometric techniques that account numerous structural breaks in series. In this extent, we created three interaction variables by captivating the impact of informal economic activities on CO2 emissions through income, energy use, and financial sector development. Besides, we built a main effect model without the interaction variables to assess the direct effects of our variables on global environmental degradation. The outcomes of the carried analyses produced supporting evidence toward the confirmation of the Environmental Kuznets Curve (EKC) assumption. Obtained findings shown that energy use, financial development and the informal economy in Turkey transmit a deteriorating impact on environmental well-being. Furthermore, the moderating role of the informal economy was found to be statistically significant factor in terms of both economic and environmental efficiency.
Because of the complexity of modern buildings—with many interconnected materials, components, and systems—fully electrifying buildings will require targeted R&D and efficient coordination across those material, component, and system levels. Because buildings that consume the smallest amount of energy are easier to electrify, energy efficiency is a crucial step toward fully electrified buildings. Materials advances will play an important role in both reducing the energy intensity of buildings and electrifying their remaining energy use. Materials are currently being explored, discovered, synthesized, evaluated, optimized, and implemented across many building components, including solid-state lighting; dynamic windows and opaque envelopes; cold climate heat pumps; thermal energy storage; heating, ventilating, and air conditioning (HVAC); refrigeration; non-vapor compression HVAC; and more. In this article, we review the current state-of-the-art of materials for various buildings end uses and discuss R&D challenges and opportunities for both efficiency and electrification.