Improved Local Weather Forecasts Using Artificial Neural Networks
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Improved Local Weather Forecasts Using Artificial Neural Networks
The Maersk Mc-Kinney Moller Institute. University of Southern Denmark, Denmark. {mgw,bnj}@mmmi.sdu.dk. Abstract. Solar irradiance and temperature ...
Improved Local Weather Forecasts Using Artificial Neural Networks Morten Gill Wollsen and Bo Nørregaard Jørgensen Centre for Energy Informatics The Maersk Mc-Kinney Moller Institute University of Southern Denmark, Denmark {mgw,bnj}@mmmi.sdu.dk
Abstract. Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show that the network outperforms the commercial forecast for lower step aheads (< 5). For larger step aheads the network’s performance is in the range of the commercial forecast. However, the neural network approach is fast, fairly precise and allows for further expansion with higher resolution.
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Introduction
Solar irradiation and temperature forecasts are in today’s world used in a variety of different control systems. One of such systems is intelligent climate control, where solar irradiance affects the planning of supplemental artificial lighting. Typically, weather forecasts for a local area are purchased from commercial weather forecast agencies. Unfortunately, forecasts are never as precise as one might hope. The Danish Meteorological Institute (DMI) states for the last running year, that they have had a 97% accuracy rate within 2◦ C [2]. The accuracy is very good, but the interval is also fairly wide. The commercial weather forecasts are typically generated in a 10 by 10km grid. If it was possible to predict the local weather based on the previous days’ weather and sufficiently fast, smaller and more precise local weather forecasts could be distributed across an area. A local forecast using previous data will learn about local variation in the weather, something that cannot be captured by a 10 by 10km grid size. This paper proposes a method that uses previous measurements to create short term (
