یک روش برنامه ریزی خطی برای حفاظت از انرژی خانگی: تخصیص موثر بودجه

Linear programming method was used to optimize the allocation of budget in order to maximize the energy savings of a hypothetical household in Turkey. The energy conservation methods involved in this study were installing photovoltaic solar cells, replacing regular windows with double-glazed ones, replacing incandescent bulbs with compact fluorescent light bulbs and replacing C-Energy Class household appliances with A-Energy Class ones. The costs of these different energy conservation methods were obtained from the manufacturers’ or distributors’ websites. The annual energy savings of these methods were either obtained from available sources or calculated when necessary. The results showed that installing double-glazed windows and purchasing compact fluorescent light bulbs are the proper choices for low budgets. When budget increased, solar panel installation emerged as the feasible choice. The findings indicated that replacing household appliances should be considered only when a budget greater than €20,000 is available. Payback periods were found to be less than one and a half years, even at the highest budget. A budget decision of €800 was found to be the optimum decision for short term investments, whereas a budget decision of €24,000 was found to be the optimum decision for long term investments.

Efficient use of energy is a very important concept, not only because it favors a more stable economy, but it also helps prevent environmental pollution, and the combination of these two facts is essential for sustainable development. Buildings are responsible for the consumption of approximately 40% of all commercial energy supplied as processed fuels or electricity in developed countries [1] and [2]. For thousands of years, mankind has tried to improve the energy efficiency of buildings via simple methods such as choosing the ideal geographic location or by using appropriate building and insulating materials depending on the climate. As the technology developed, the measures to minimize energy loss have become more complex. However, it was the 1973 global energy crisis that triggered a worldwide pursuit in designing buildings with less energy consumption, by incorporating energy efficiency and renewable energy resources [3]. Since then, many countries adopted laws and regulations on how to use energy more efficiently and energy efficiency in residential and commercial buildings have become a common area of interest [4], [5], [6], [7] and [8]. Above-mentioned laws and regulations impose certain targets and deadlines regarding both residential and commercial buildings with the aim of reaching specified energy consumption limits. For instance, in Turkey the Energy Efficiency Law came into effect in May 2007, aiming to minimize the high level of energy intensity so as to achieve productive and effective use in every field of energy, prevention of wasteful expenditure and protection of the environment. This law, which was revised recently in February 2011, comprises the principals and procedures in order to increase the energy efficiency in industrial, building and transport sectors. With this law, Turkey aims to accomplish an energy saving of 30% in the next decade [9]. In order to achieve this goal, home-owners or tenants must take suitable actions to reduce their energy consumption without having to compensate from their quality of living. Therefore, the solution must satisfy energy-related, environmental and financial aspects of the problem. When the great number of possible actions that can reduce the energy consumption of a building is concerned, using a sophisticated method to determine the optimum choice(s) is inevitable. Before proceeding with linear programming, which is our choice of method, we would like to briefly review similar studies in the field.

In this study, linear programming method was used to maximize energy savings subject to budget for a hypothetical household in Turkey. The subject house was selected to be a two-floor, detached building. The methods involved to decrease the building's energy consumption were installing photovoltaic solar panels on the roof, replacing incandescent light bulbs with compact fluorescent light bulbs, installing double-glazed windows and replacing C-Energy Class appliances (refrigerator, washing machine and dishwasher) with A-Energy Class versions. The physical constraints of the house in regards to the above-mentioned methods were as follows: • a total window area 32 m2 • total roof area allocated for solar panel installation: 70 m2 • total number of light bulbs to be replaced: 10 The energy savings were calculated in power units (Watts). Energy savings induced by each individual method were either obtained from manufacturer or distributor companies’ websites, or calculated where applicable. The purchase and installation costs of each of the energy saving methods were obtained by taking the averages of various values gathered from the manufacturer or distributor companies’ websites. Lingo 12.0 software was used to for linear optimization. The energy savings were calculated as a function of total allowable budget, and budgets ranging between €400 and €40,000 were used as inputs for the model. The results indicated that installing photovoltaic solar panels is the optimum choice throughout the entire budget range, as a result of the high energy saving opportunity. Renewing household appliances did not emerge as very profitable options, due to the low energy savings when compared to other techniques. Double-glazed window installation and purchasing compact fluorescent light bulbs was the optimum combination because of the relatively low cost. For the given constraints the maximum amount of energy savings was found to be 19,496.9 W, at a budget of €28,804.8 As the authors, we believe that the most significant contribution of this particular work to building energy research is the methodology developed rather than the results themselves. The model we presented can be modified as desired for different households, climate conditions, or countries so that the final results would be completely different. The reason we decided to implement the model by using data obtained from local sources was to ensure consistency, yet we believe the model can be applied globally as long as the required data can be provided.