Load Forecasting. Conference Paper. Effect of the Training Data Quantity on the Day-ahead Load Forecasting Performance in the Industrial Sector. December 2023.
This paper proposes a day-ahead industrial load forecasting model employing load change rate features and combining the firefly algorithm to optimize the ...
Missing: Quantity Sector.
A new method is presented, to derive an algorithm that provides a forecast of one-day-ahead electricity consumption of a building.
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The GRU improves the forecast by 6.23 % compared to the second-best algorithm implemented, a combination of GRU and Long short-term memory (LSTM). From a ...
We investigate the performance of a special case of STLF, namely transfer learning (TL), by considering a set of 27 time series that represent the national day ...
This paper studies how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for ...
Dec 11, 2021 · This paper presents an online refinement strategy for day-ahead forecasting using intraday data for a campus-level load, focusing on self-adapting correction ...
This thesis will define. STLF as a 24-hour-ahead load forecast whose results will provide an hourly electric load forecast in kilowatts (kW) for the future 24 ...
The sister forecasts had two main applications: (1) improve load forecasts via combining sister forecasts; and (2) generate accurate probabilistic load ...
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The long-term load forecast sets out the 10-year projections of electric energy usage and seasonal peak demand in New England.