Abstract
Temperature control and its prediction has turned into a research challenge for the knowledge of the planet and its effects on different human activities and this will assure, in conjunction with energy efficiency, a sustainable development reducing CO2 emissions and fuel consumption. This work tries to offer a practical solution to temperature forecast and control, which has been traditionally carried out by specialized institutes. For the accomplishment of temperature estimation, a score fusion block based on Artificial Neural Networks was used. The dataset is composed by data from a meteorological station, using 20,000 temperature values and 10,000 samples of several meteorological parameters. Thus, the complexity of the traditional forecasting models is resolved. As a result, a practical system has been obtained, reaching a mean squared error of 0.136 °C for short period of time prediction and 5 °C for large period of time prediction.
Highlights
Modern societies are conditioned by many natural factors, such as, for example, the weather
The meteorological changes affect to different aspects of our lives. These changes have influence over the fields which are more directly connected with climatology, as the agricultural sector, and over others which are more complex and apparently dissociated from atmospheric reality, as energy efficiency, and this becomes in an important factor in planning for sustainable urban development
They have been obtained by diverse benchmarks tests, in which the Artificial Neural Networks (ANN) has been subjected to different configurations by modifying the sliding window, the number of hidden neurons and the number of training patterns parameters
Summary
Modern societies are conditioned by many natural factors, such as, for example, the weather. The meteorological changes affect to different aspects of our lives These changes have influence over the fields which are more directly connected with climatology, as the agricultural sector, and over others which are more complex and apparently dissociated from atmospheric reality, as energy efficiency, and this becomes in an important factor in planning for sustainable urban development. There are currently three important trends which tackle this theme: Climatology, acting as empirical tradition; Physics of the Atmosphere, as theoretical tradition; or Numerical Weather Prediction (NWP), as modern tradition [2]. These recent mathematical models struggle at present to achieve a shorter response time as main target. The more accurate the method is, the more data is required and a longer response time is obtained
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