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The optimization problem is set up based on daily load demand (such as electricity, heat, and cooling) and time-of-use (TOU) energy prices.
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Efficacy measures were based on daily diaries (recorded via IVRS).
*Estimates are based on daily data.
All calculations were based on daily data.
While most methods available in the literature address some specific network loading conditions, e.g. average, light-load or heavy-load, the proposed algorithm is based on the daily load variation curve.
Meanwhile, the model is simulated based on the daily load curve.
The method is based on comparing typical daily load profiles for the same (or close) date in two different years and different weather conditions.
The integrated system is designed based on the daily wind load.
In the current paper, we introduce monthly and daily factors based on load type to replace the demand level: S_{i,m,t} = S_{i,m,b} F_{m,t} F_{D,t} (1 + varepsilon )^{T} R (18 where S i,m,b is the base power of node i at m th month; F m,t is the monthly factor whiles F D,t is the daily factor; ε is the annual increment of load and R is the random factor.
Therefore, if a month ahead average daily electricity price profile can be forecasted, retailors can set the month-to-month variable rate plan based on the load profiles of the customers.
The CMS dosing schedule was based on a loading dose of 9 MU and a 9-MU twice-daily fractioned maintenance dose, titrated on renal function.
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