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Jun 10, 2024 · This paper introduces a residual bootstrap algorithm to generate interval estimates of day-ahead electricity demand. A machine learning algorithm is used to ...
Jun 10, 2024 · This algorithm is evaluated on the real electricity demand data from EULR(End Use Load Research) and is compared to other bootstrapping methods for varying ...
Jun 12, 2024 · The research findings suggest that the EMD framework shows promise for accurately forecasting energy demand within specific intervals.
6 days ago · Interval prediction is a promising method that can reveal the uncertainty of building load and has been shown to effectively manage building energy systems.
2 days ago · In this study, a deep-learning-based interval forecasting model is developed by combining fuzzy information granulation, attention mechanism, and long short- ...
Jun 13, 2024 · Abstract Any supervised machine learning analysis is required to provide an estimate of the out-of- sample predictive performance. However, it is imperative ...
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6 days ago · [19] proposed a short-term PV generation forecasting method based on time series expansion and high-order fuzzy cognitive maps, which reduced the influence of ...
Jun 14, 2024 · In order to further improve the load prediction accuracy of each consumer in the region, this paper proposes a short-term prediction method of electric load ...
Jun 5, 2024 · This study aims to address the issues of uncertainties, inefficiencies, and lack of intelligence in the mechanisms and methods for determining the ρdmax. To ...
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May 29, 2024 · Many AI applications and statistical inference techniques involving temporal data can be formulated in a Bayesian space-kime analytics framework. We explore ...
Missing: Electricity Demand: