4,181 research outputs found
The inhabited environment, infrastructure development and advanced urbanization in China's Yangtze River Delta Region
This paper analyzes the relationship among the inhabited environment, infrastructure development and environmental impacts in China's heavily urbanized Yangtze River Delta region. Using primary human environment data for the period 2006-2014, we examine factors affecting the inhabited environment and infrastructure development: urban population, GDP, built-up area, energy consumption, waste emission, transportation, real estate and urban greenery. Then we empirically investigate the impact of advanced urbanization with consideration of cities' differences. Results from this study show that the growth rate of the inhabited environment and infrastructure development is strongly influenced by regional development structure, functional orientations, traffic network and urban size and form. The effect of advanced urbanization is more significant in large and mid-size cities than huge and mega cities. Energy consumption, waste emission and real estate in large and mid-size cities developed at an unprecedented rate with the rapid increase of economy. However, urban development of huge and mega cities gradually tended to be saturated. The transition development in these cities improved the inhabited environment and ecological protection instead of the urban construction simply. To maintain a sustainable advanced urbanization process, policy implications included urban sprawl control polices, ecological development mechanisms and reforming the economic structure for huge and mega cities, and construct major cross-regional infrastructure, enhance the carrying capacity and improvement of energy efficiency and structure for large and mid-size cities
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Intelligent energy management system - techniques and methods
ABSTRACT
Our environment is an asset to be managed carefully and is not an expendable resource to be taken for granted. The main original contribution of this thesis is in formulating intelligent techniques and simulating case studies to demonstrate the significance of the present approach for achieving a low carbon economy. Energy boosts crop production, drives industry and increases employment. Wise energy use is the first step to ensuring sustainable energy for present and future generations. Energy services are essential for meeting internationally agreed development goals. Energy management system lies at the heart of all infrastructures from communications, economy, and society’s transportation to the society. This has made the system more complex and more interdependent. The increasing number of disturbances occurring in the system has raised the priority of energy management system infrastructure which has been improved with the aid of technology and investment; suitable methods have been presented to optimize the system in this thesis.
Since the current system is facing various problems from increasing disturbances, the system is operating on the limit, aging equipments, load change etc, therefore an improvement is essential to minimize these problems. To enhance the current system and resolve the issues that it is facing, smart grid has been proposed as a solution to resolve power problems and to prevent future failures. This thesis argues that smart grid consists of computational intelligence and smart meters to improve the reliability, stability and security of power. In comparison with the current system, it is more intelligent, reliable, stable and secure, and will reduce the number of blackouts and other failures that occur on the power grid system. Also, the thesis has reported that smart metering is technically feasible to improve energy efficiency.
In the thesis, a new technique using wavelet transforms, floating point genetic algorithm and artificial neural network based hybrid model for gaining accurate prediction of short-term load forecast has been developed. Adopting the new model is more accuracy than radial basis function network. Actual data has been used to test the proposed new method and it has been demonstrated that this integrated intelligent technique is very effective for the load forecast.
Choosing the appropriate algorithm is important to implement the optimization during the daily task in the power system. The potential for application of swarm intelligence to Optimal Reactive Power Dispatch (ORPD) has been shown in this thesis. After making the comparison of the results derived from swarm intelligence, improved genetic algorithm and a conventional gradient-based optimization method, it was concluded that swam intelligence is better in terms of performance and precision in solving optimal reactive power dispatch problems
Comparitive assessment of the vulnerability and resilience of 10 deltas, synthesis report
The proposed framework for delta assessment and especially the scorecards are intended to enhance awareness raising, discussion and prioritization on most relevant delta issues, in each delta but also in comparison with other deltas. This should lead to more efficient and effective (multi-sectoral) policy formulation, management design and implementation, in concrete Delta plans, pilot-projects and (research) programmes. The target groups are all stakeholders who are involved in delta management at different levels and with different interests (government, private companies, NGOs, public), and who wish to contribute to the resilience of their own delta and other deltas worldwide
Prediction of internal temperatures during hot summer conditions with time series forecasting models
A novel application using adaptive autoregressive time series forecasting with exogenous inputs (i.e. ARX) has been developed in order to provide reliable short-term
forecasts of the internal temperatures in dwellings during hot summer conditions (i.e. heatwaves). The study shows that with proper selection of the predictors, based on the
Akaike Information Criterion (AIC), the forecasts provide acceptable accuracy for periods up to 72 hours. The hourly results for the analysed dwellings showed a Mean
Absolute Error (MAE) below 0.63°C and 0.49°C for the two case study dwellings across the 3-day forecasting period, during the 2015 heatwave. These findings point to the potential for using time series forecasting as part of an overheating warning system in buildings, especially those housing vulnerable occupants
U-shaped Transformer: Retain High Frequency Context in Time Series Analysis
Time series prediction plays a crucial role in various industrial fields. In
recent years, neural networks with a transformer backbone have achieved
remarkable success in many domains, including computer vision and NLP. In time
series analysis domain, some studies have suggested that even the simplest MLP
networks outperform advanced transformer-based networks on time series forecast
tasks. However, we believe these findings indicate there to be low-rank
properties in time series sequences. In this paper, we consider the low-pass
characteristics of transformers and try to incorporate the advantages of MLP.
We adopt skip-layer connections inspired by Unet into traditional transformer
backbone, thus preserving high-frequency context from input to output, namely
U-shaped Transformer. We introduce patch merge and split operation to extract
features with different scales and use larger datasets to fully make use of the
transformer backbone. Our experiments demonstrate that the model performs at an
advanced level across multiple datasets with relatively low cost
Comparative assessment of the vulnerability and resilience of 10 deltas : work document
Background information about: Nile delta (Egypt), Incomati delta (Mozambique), Ganges-Brahmaputra-Meghna (Bangladesh), Yangtze (China), Ciliwung (Indonesia), Mekong (Vietnam), Rhine-Meuse (The Netherlands), Danube (Romania), California Bay-Delta, Mississippi River Delta (USA
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