The final response curves plotted on Matlab

This was a part of my mini term project for soft computing course. For some reason this has been quite popular on the web as a receive a lot of mails concerning it (mostly because they cannot access the source code attached within the PDF!). The basic idea over which the paper is proposed is that only temperature never decides comfort level, it is the combined effect of temperature, humidity and wind speed that can faithfully decide if a certain set point will be comfortable. So this is a fuzzy logic inspired  method to find that optimal point while ensuring lowest energy consumption. Excerpt from the paper -
Air conditioners and air conditioning systems are integral part of almost every institution. They contribute significant part of total energy consumption. Studies suggest that in locations like auditoriums, indoor stadiums and conference halls, air conditioning can contribute as much as 75% of total energy intake. Even in homes and offices, power consumed by air conditioners is significant. In this paper a scheme has been proposed to maintain the temperature and the humidity close to the targeted values, and reduce the electrical energy intake of the AC compressor/Fan while utilizing all available resources in the most efficient manner.
Problem Definition
The task of dehumidification and temperature decrease goes hand in hand in case of conventional AC. Once target temperature is reached AC seizes to function like a dehumidifier. Also complex interactions between user preferences, actual room temperature and humidity level are very difficult to model mathematically. But in this work this limitation has been taken into cogitation and overcome to a great extent using fuzzy logic to represent the intricate influences of all these parameters. The optimal limits of comfort zone, typically marked at a temperature of 25°C and dew point 11°C, are used as the targets. Conventional AC system controls humidity in its own way without giving the users any scope for changing the set point for the targeted humidity unlike the scope it offers to change the set point for the targeted temperature through a thermostat. This causes a significant level of flexibility as well as efficiency loss especially in hot and humid countries like India. For instance at higher humidity level (say at dew point 18°C) an occupant may perceive same comfort level at 22ºC as he would perceive at 26ºC at dew point 15°C. This translates to huge energy and monitory saving in terms of reduced compressor/fan duty cycle. In the developed scheme, the sensor captured temperature, user temperature preference and humidity readings are fuzzified. These are used to decide the fuzzy qualifier, which is decoded into a crisp value that in turn controls different aspects of the AC. In the problem dew point (Td) temperature is used to measure humidity instead of relative humidity (RH), this is because RH is a function of both temperature and moisture content while Td is a function of moisture content only. Hence it becomes very easy to model comfort level on the basis of Td.
Read the complete paper here:  Fuzzy Logic Control of Air Conditioners sample code can be downloaded from here

Schematics of the system

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