A novel hybrid model based on particle swarm optimisation and extreme learning machine for short-term temperature prediction using ambient sensors

Abstract In modern buildings which are becoming smart day by day, indoor temperature can be forecasted with the data obtained from outfitted sensors. Predictive models based on data can accurately forecast temperature which further saves energy by optimisation of resources and technologies such as heating, ventilation and air conditioners for making the atmosphere conducive and comfortable in the ambient environment. This paper discusses an experiment from such house fitted with sensors for different parameter affecting indoor temperature. We propose a hybrid model which is based on particle swarm optimisation and emerging extreme learning machine to forecast the temperature to optimise the use of energy further. The proposed hybrid model also includes the variant online sequential extreme learning machine(OSELM) which accepts data online and is adaptive to the changing environmental conditions. We perform experiment based on many sensors combinations affecting temperature using particle swarm optimization and statistical tools to determine their relevance and correlation with temperature and compare results with conventional methods. Proposed methods improved the accuracy of the forecasting and also generalisation performance over other methods on the same dataset.

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