• Energy Research
• 2016-2025close
• 13. Climate action close
• 8. Economic growth close
• Research Repository of Catalonia close

Most of the Catalonian population and urban areas are located along the Mediterranean coastal zone and for this analysis three largest Catalonia’s cities are chosen: Barcelona, Terrassa and Tarragona. Each city is a geographical representative of a different climate type according to the re-analysed Köppen-Geiger climate classification. The objective of this research is to demonstrate the effects of regional climate variations on the potential of natural ventilation (NV) application in building design. The first part of the article presents the availability of NV corresponding to yearly climate conditions applying the methodology “Climate Potential for Natural Ventilation” (CPNV). In continuation, the second analysis is calculated with building energy simulation software and displays possible refrigeration energy savings in three geographical locations. Mixed-mode (or hybrid-mode) and night-ventilation techniques are applied for the cooling process of a hypothetical low-rise office building model. Peer Reviewed

Most of the Catalonian population and urban areas are located along the Mediterranean coastal zone and for this analysis three largest Catalonia’s cities are chosen: Barcelona, Terrassa and Tarragona. Each city is a geographical representative of a different climate type according to the re-analysed Köppen-Geiger climate classification. The objective of this research is to demonstrate the effects of regional climate variations on the potential of natural ventilation (NV) application in building design. The first part of the article presents the availability of NV corresponding to yearly climate conditions applying the methodology “Climate Potential for Natural Ventilation” (CPNV). In continuation, the second analysis is calculated with building energy simulation software and displays possible refrigeration energy savings in three geographical locations. Mixed-mode (or hybrid-mode) and night-ventilation techniques are applied for the cooling process of a hypothetical low-rise office building model. Peer Reviewed

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

AbstractFiscal rules are necessary to protect monetary policy from the consequences of unsustainable or active fiscal policy for inflation. Monetary unions, such as the Economic and Monetary Union (EMU), require even stronger fiscal rules to avoid free riding by regional fiscal authorities on the common monetary policy. By contrast, in a fiscal federation, the federal government internalises the effect of active regional policies on the overall price level. Federal fiscal policy contributes to price stability either by enforcing fiscal rules or by adjusting its own stance. Following Canzoneri, Cumby and Diba (2001), we test whether federal and regional governments in Germany behave in an active or passive way. We find evidence of a spillover effect of unsustainable policies on other regions. The German federal government offsets the effect on the price level by running passive policies. The Bundesbank's prime objective of price stability is therefore endorsed by fiscal policy. The results have implications for the regulation of fiscal policies in the EMU.

AbstractFiscal rules are necessary to protect monetary policy from the consequences of unsustainable or active fiscal policy for inflation. Monetary unions, such as the Economic and Monetary Union (EMU), require even stronger fiscal rules to avoid free riding by regional fiscal authorities on the common monetary policy. By contrast, in a fiscal federation, the federal government internalises the effect of active regional policies on the overall price level. Federal fiscal policy contributes to price stability either by enforcing fiscal rules or by adjusting its own stance. Following Canzoneri, Cumby and Diba (2001), we test whether federal and regional governments in Germany behave in an active or passive way. We find evidence of a spillover effect of unsustainable policies on other regions. The German federal government offsets the effect on the price level by running passive policies. The Bundesbank's prime objective of price stability is therefore endorsed by fiscal policy. The results have implications for the regulation of fiscal policies in the EMU.