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Icing losses encountered in a wind farm are very hard to assess as they can vary by a factor of three from year to year. This inter-annual variation is a critical issue from the assessment phase to the operation of wind turbines in cold climate. To mitigate the effects of this issue, a proposed solution is to define an ice index to yield a reference for icing conditions based on historical values. Knowing that icing frequency has an inter-annual variation 5 times greater than the wind speed, the definition of an ice index is long overdue. This paper proposes a simple and accessible method for defining an ice index based on cloud base height measured at a weather station. A similar strategy is already used in the industry for other purposes such as estimating the long-term average icing frequency in icing maps. The novelty of the proposed method lies in the description of the variation in icing frequency rather than its long-term average. The ice index proposed in this paper is shown to be representative of the icing losses in 6 wind farms located in eastern Canada. Moreover, it was also shown that the ice index is strongly correlated to the icing measurements made on wind turbine nacelle. In this paper, the yearly and monthly ice indexes, from September 2007 to December 2021, are shared. These indexes are based on the meteorological data from Valcartier weather station near Quebec City. This definition of an ice index directly responds to a critical need of the wind industry in cold climate, a common reference for icing frequency. Ice indexes could be publicly shared each month in cold climate areas to guide wind farm owners and operators in their decisions and analysis of performances. Such an index could also be used to asses the long-term variation of the ice index and the effect of climate change on icing frequency and impacts on the wind energy production in cold climate. • Definition of an icing frequency index based on publicly available could base height measurements. • Relationship between the ice index and icing losses in six wind farms. • Relationship between the ice index and the meteorological icing frequency measured on a wind turbine nacelle. [ABSTRACT FROM AUTHOR]

Through tests conducted at the Toronto and Region Conservation Authority's (TRCA) Archetype Sustainable Houses, a hybrid residential space heating system with a supervisory controller was monitored and studied to evaluate its performance and effectiveness for the heating season. A high efficiency natural gas furnace (NGF) and an electric air source heat pump (ASHP) were coupled together to meet the space heating demand of the house. This integrated system is called the cloud based Smart Dual Fuel Switching System (SDFSS) that considers time-of-use (TOU) pricing, fuel cost, short-term weather forecast, and equipment efficiencies and capacities. This multi-variable decision-making process defines an optimal schedule for the hybrid system to run more cost effectively. This paper analyses two separate SDFSSs. According to these analyses, the SDFSS systems showed lower operating cost with respect to the furnace or the ASHP system alone with various carbon tax (CT) levels from $0 to $250/tonne of CT with an increment of $10/tonne of CT that were simulated, along with a significant GHG emission reduction relative to the conventional heating systems. Furthermore, with these technologies, Canada's residential sector could potentially meet Canada's Paris Agreement goals. The SDFSS technology is a is flexible, user friendly, ubiquitous technology for the smooth transition from today's natural gas dominated space heating system to the future's low carbon infrastructure powered by a heat pump and renewable energy. [ABSTRACT FROM AUTHOR]