ALBERT

Description: The Himalayan mountain forest ecosystem has been degrading since the British ruled the area in the 1850s. Local understanding of the patterns and processes of degradation is desperately required to devise management strategies to halt this degradation and provide long-term sustainability. This work comprises a satellite image based study in combination with national expert validation to generate sub-district level statistics for forest cover over the Western Himalaya, Pakistan, which accounts for approximately 67% of the total forest cover of the country. The time series of forest cover maps (1990, 2000, 2010) reveal extensive deforestation in the area. Indeed, approximately 170,684 ha of forest has been lost, which amounts to 0.38% per year clear cut or severely degraded during the last 20 years. A significant increase in the rate of deforestation is observed in the second half of the study period, where much of the loss occurs at the western borders along with Afghanistan. The current study is the first systematic and comprehensive effort to map changes to forest cover in Northern Pakistan. Deforestation hotspots identified at the sub-district level provide important insight into deforestation patterns, which may facilitate the development of appropriate forest conservation and management strategies in the country.

Description: Energies, Vol. 10, Pages 1579: Energy Consumption Forecasting for University Sector Buildings Energies doi: 10.3390/en10101579 Authors: Khuram Pervez Amber Muhammad Waqar Aslam Anzar Mahmood Anila Kousar Muhammad Yamin Younis Bilal Akbar Ghulam Qadar Chaudhary Syed Kashif Hussain Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore, a simple, easier, less time consuming and reliable forecasting model which could be used for different types of buildings is desired. In this paper, we have presented a forecasting model based on five years of real data sets for one dependent variable (the daily electricity consumption) and six explanatory variables (ambient temperature, solar radiation, relative humidity, wind speed, weekday index and building type). A single mathematical equation for forecasting daily electricity usage of university buildings has been developed using the Multiple Regression (MR) technique. Data of two such buildings, located at the Southwark Campus of London South Bank University in London, have been used for this study. The predicted test results of MR model are examined and judged against real electricity consumption data of both buildings for year 2011. The results demonstrate that out of six explanatory variables, three variables; surrounding temperature, weekday index and building type have significant influence on buildings energy consumption. The results of this model are associated with a Normalized Root Mean Square Error (NRMSE) of 12% for the administrative building and 13% for the academic building. Finally, some limitations of this study have also been discussed.

Description: Energies, Vol. 11, Pages 1133: Techno, Economic and Environmental Assessment of a Combined Heat and Power (CHP) System—A Case Study for a University Campus Energies doi: 10.3390/en11051133 Authors: Khuram Pervez Amber Tony Day Naeem Iqbal Ratyal Adnan Khalid Kiani Rizwan Ahmad Universities in the United Kingdom that have installed Combined Heat and Power (CHP) technology are making good moves towards achieving their CO2 reduction targets. However, CHP may not always be an economical option for a university campus due to numerous factors. Identification of such factors is highly important before making an investment decision. A detailed technical, economic, and environmental feasibility of CHP is, therefore, indispensable. This study aims to undertake a detailed assessment of CHP for a typical university campus and attempts to highlight the significance of such factors. Necessary data and information were collected through site visits, whereas the CHP sizing was performed using the London South Bank University (LSBU) CHP model. The results suggest that there is a strong opportunity of installing a 230 kW CHP that will offset grid electricity and boilers thermal supply by 47% and 75%, respectively, and will generate financial and environmental yearly savings of £51k and 395 t/CO2, respectively. A wider spark gap decreases the payback period of the project and vice versa. The capital cost of the project could affect the project’s economics due to factors, such as unavailability of space for CHP, complex existing infrastructure, and unavailability of a gas connection.

Description: Sustainability, Vol. 10, Pages 2069: The Significance of a Building’s Energy Consumption Profiles for the Optimum Sizing of a Combined Heat and Power (CHP) System—A Case Study for a Student Residence Hall Sustainability doi: 10.3390/su10062069 Authors: Khuram Pervez Amber Antony R. Day Naeem Iqbal Ratyal Rizwan Ahmad Muhammad Amar University buildings, such as student residence halls with year-round consistent energy demands, offer strong opportunities for Combined Heat and Power (CHP) systems. The economic and environmental feasibility of a CHP project is strongly linked with its optimum sizing. This study aims to undertake such an assessment for a CHP system for a student residence hall located in London, the United Kingdom (UK). The study also aims to undertake a sensitivity analysis to investigate the effect of different parameters on the project’s economics. Necessary data are collected via interviews with the University’s Energy Manager. Modeling of the CHP system is performed using the London South Bank University (LSBU, London, the UK) CHP model. Results demonstrate that optimum sizing of CHP is crucial for achieving higher economic and environmental benefits and strongly depends on the authenticity of the energy consumption data, based on which the CHP is being sized. Use of incorrect energy data could result in an undersized or oversized CHP system, where an oversized system will result in higher negative results compared to an undersized system. Finally, Monto Carlo statistical analysis shows that electricity price is the significant factor that could affect the project’s economics. With an increasing spark gap, the payback period decreases, and vice versa.

Description: Smart grids (SGs), as an emerging grid modernization concept, is spreading across diverse research areas for revolutionizing power systems. SGs realize new key concepts with intelligent technologies, maximizing achieved objectives and addressing critical issues that are limited in conventional grids. The SG modernization is more noticeable at the distribution grid level. Thus, the transformation of the traditional distribution network (DN) into an intelligent one, is a vital dimension of SG research. Since future DNs are expected to be interconnected in nature and operation, hence traditional planning methods and tools may no longer be applicable. In this paper, the smart distribution network (SDN) concept under the SG paradigm, has presented and reviewed from the planning perspective. Also, developments in the SDN planning process have been surveyed on the basis of SG package (SGP). The package presents a SDN planning foundation via major SG-enabling technologies (SGTF), anticipated functionalities (SGAF), new consumption models (MDC) as potential SDN candidates, associated policies and pilot projects and multi-objective planning (MOP) as a real-world optimization problem. In addition, the need for an aggregated SDN planning model has also been highlighted. The paper discusses recent notable related works, implementation activities, various issues/challenges and potential future research directions; all aiming at SDN planning.

Description: Energies, Vol. 11, Pages 94: Heating and Cooling Degree-Days Maps of Pakistan Energies doi: 10.3390/en11010094 Authors: Khuram Amber Muhammad Aslam Faraz Ikram Anila Kousar Hafiz Ali Naveed Akram Kamran Afzal Haroon Mushtaq The building sector consumes about 40% of the world’s primary energy. Seasonal climatic conditions have a significant effect on the energy consumption in buildings. One of the famous methods used for decoding this seasonal variation in buildings energy consumption is the “Degree Days Method”. Data has been widely published for the heating and cooling degree days of different countries. Unfortunately, there is very limited and outdated published data for the heating and cooling degree-days of Pakistan. In this study, yearly average heating and cooling degree-days for different regions of Pakistan are established by using 30 year long-term measured data for different base temperatures. The data is presented in tables and figures whereas heating and cooling degree-day maps of Pakistan have been developed.