Abstract
A short-term forecast of energy consumption is affected by different factors related to the demand in residential, commercial, thermoelectric, and industrial sectors. This demand can be strongly constrained by weather variables, especially temperatures, whose forecast may be very useful to predict the balances between supply and demand, minimizing the risk of price volatility. Energy companies use the relationship between meteorological forecast output and energy request to provide an effective scheduling of national gas and power grids and reduce operational costs in critical periods. This work reports a comparison analysis for short- and medium-term daily temperature forecasts during the period 2013-2014 by using the weather model e-kmf™ (eni-kassandra meteo forecast), currently adopted in gas and power applications where meteorological output has a key role. This weather forecast system uses different models and initial data to develop probabilistic predictions from a perspective of eleven days ahead. In particular, a set of model runs with horizontal grid spacing of 5.5, 8, 13, and 18 km with the same domain size are undertaken to assess the sensitivity of temperature to horizontal resolutions. A nonlinear Kalman filter has been also applied to postprocess forecasted data in eight European cities (Milano, Roma, Torino, Napoli, Munich, Paris, Brussels, and London). Filtered forecasts over these cities have been compared to local observations taken from SYNOP (surface synoptic observations) and METAR (meteorological Aerodrome Report) stations. Skill scores of performance have been used to generally assess the forecast reliability up to day +11. In order to understand the sensitivity to the horizontal resolution, investigations have been carried out even during four specific periods of two weeks with stable and unstable weather conditions.
Highlights
Energy consumption is strictly correlated to seasonal weather and climatic trends, in particular, for surface temperature and precipitation [1, 2]
Observed temperature data are available for the years 2013 and 2014, when particular weather periods are selected to be analysed over the selected cities
Is fortnight is referred to stable and unstable time, where stable means that the meteorological pattern may be generally referred to a presence of a high pressure at synoptic scale with limited temperature changes between the following days, while unstable is referred to a relatively occurrence of low pressure or changeable weather due to the passage of fronts and instabilities with significant variations in temperatures among consecutive days
Summary
Received 21 December 2018; Revised 13 March 2019; Accepted 22 April 2019; Published 8 May 2019. TM analysis for short- and medium-term daily temperature forecasts during the period 2013-2014 by using the weather model e-kmf (eni-kassandra meteo forecast), currently adopted in gas and power applications where meteorological output has a key role. Is weather forecast system uses different models and initial data to develop probabilistic predictions from a perspective of eleven days ahead. A nonlinear Kalman filter has been applied to postprocess forecasted data in eight European cities (Milano, Roma, Torino, Napoli, Munich, Paris, Brussels, and London). Filtered forecasts over these cities have been compared to local observations taken from SYNOP (surface synoptic observations) and METAR (meteorological Aerodrome Report) stations. In order to understand the sensitivity to the horizontal resolution, investigations have been carried out even during four specific periods of two weeks with stable and unstable weather conditions
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