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In order to present core issues usually addressed under sustainable urbanism, the paper makes a broad scanning of a number of relevant documents dealing with sustainable development. A wider selection has been made reflecting the major segments of the EU policy in this area, the standpoints of professional bodies on sustainable cities and of educational association of planning on issues of sustainable development. This overview has been complemented with the strategy of UNESCO on teaching and learning for a sustainable future. The overview shows a continuous increase of sensitivity towards spatial/urban issues which could be dealt with in a successful way only by urban planners who could supply innovative solutions based on a participatory and inclusive process of planning. Higher education institutions in the field of urban planning must respond adequately to these challenges by strengthening their disciplinary profiles and expertise on which to build their interdisciplinary interconnectedness with other disciplines in the arena of sustainability. In this process, the approaches chosen in teaching sustainable urbanism will be of great importance as they will influence the learners and prepare them to react to the ever changing circumstances of everyday life. The paper proposes a conceptual framework of sustainable urbanism and suggests a number of approaches to teaching and learning that could respond to this challenge. [ABSTRACT FROM AUTHOR]

Electricity consumption in the administration buildings of a typical higher education campus in the UK accounts for 26% of the campus annual electricity consumption. A reliable forecast of electricity consumption helps energy managers in numerous ways such as in preparing future energy budgets and setting up energy consumption targets. In this paper, we developed two models, a multiple regression (MR) model and a genetic programming (GP) model to forecast daily electricity consumption of an administration building located at the Southwark campus of London South Bank University in London. Both models integrate five important independent variables, i.e. ambient temperature, solar radiation, relative humidity, wind speed and weekday index. Daily values of these variables were collected from year 2007 to year 2013. The data sets from year 2007 to 2012 are used for training the models while 2013 data set is used for testing the models. The predicted test results for both the models are analyzed and compared with actual electricity consumption. At the end, some conclusions are drawn about the performance of both models regarding their forecasting capabilities. The results demonstrate that the GP model performs better with a Total Absolute Error (TAE) of 6% compared to TAE of 7% for MR model. [ABSTRACT FROM AUTHOR]