In India, there are various codes, standards, guidelines and rating systems launched to make energy intensive and large sized buildings energy efficient whereas independent residential buildings are not covered even though they exist most in numbers of total housing stock. This paper presents a case study methodology for energy performance assessment of existing residential stock of Hamirpur that can be used to develop suitable energy efficiency regulations. The paper discusses the trend of residential development in Hamirpur followed by classification based on usage, condition, predominant material use, ownership size and number of rooms, source of lighting, assets available, number of storey and plot sizes using primary and secondary data. It results in identification of predominant materials used and other characteristics in each of urban and rural area. Further cradle to site embodied energy index of various dominant building materials and their market available alternative materials is calculated from secondary literature and by calculating transportation energy. One representative existing building is selected in each of urban and rural area and their energy performance is evaluated for material embodied energy and operational energy using simulation. Further alternatives are developed based on other dominant materials in each area and evaluated for change in embodied and operational energy. This paper identifies the energy performance of representative houses for both areas and in no way advocates the preference of one type over another. The paper demonstrates a methodology by which energy performance assessment of houses shall be done and also highlights further research.
The building sector along with its sub-sectors is the world’s leading consumer of all forms of energy and consumes about 30% of the world’s final energy consumption. With the increase in population, industrialization and urbanization trend, the energy sector has grown rapidly in the past decades to cater to the increase in energy demand. To cater this, many regulatory efforts in the form of energy efficiency and conservation codes offering guidelines and measures during and post design have been proposed in different countries of the world. In order to optimize the consumption of energy and achieving energy efficiency in buildings, an important role is played by the energy consumption prediction. In the due course of time, various energy prediction models have been developed by researchers, which are either based on Physical, Hybrid, or Data-driven methods. In this paper, the energy prediction results using two mostly used data-driven methods i.e. multi linear regression and artificial neural network methods, are compared. For this energy consumption data was collected and monitored from six dwelling units when the outside temperature range is 4°C to 15 °C. It has been observed that the monthly energy consumption predicted using ANN method is more accurate than the prediction using MLR. The variation in energy prediction values ranges between -0.302% to +1.752% in ANN, whereas in MLR it ranges between -2.112% to +2.448%.
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