A qualitative study of probability density visualization techniques in measurements

Fuentes, Lee Gonzales; Barbé, Kurt; Barford, Lee; Lauwers, Lieve; Philips, L.

doi:10.1016/j.measurement.2014.12.022

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…, where IQR is the inter-quartile range and N the sample size. An illustrative review explaining KDE and describing the advantages of this method can be found in Gonzales-Fuentes et al (2015).…”

Section: Modulation Intensitymentioning

confidence: 99%

A Systematic Comparison of Tropical Waves over Northern Africa. Part I: Influence on Rainfall

et al. 2019

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Low-latitude rainfall variability on the daily to intraseasonal timescale is often related to tropical waves, including convectively coupled equatorial waves, the Madden-Julian Oscillation (MJO), and tropical disturbances. Despite the importance of rainfall variability for vulnerable societies in tropical Africa, the relative influence of tropical waves for this region is largely unknown. This article presents the first systematic comparison of the impact of six wave types on precipitation over northern tropical Africa during the transition and full monsoon seasons, using two satellite products and a dense rain gauge network.Composites of rainfall anomalies in the different datasets show comparable modulation intensities in the West Sahel and at the Guinea Coast, varying from less than 2 to above 7 mm d −1 depending on the wave type. African Easterly Waves (AEWs) and Kelvin waves dominate the 3-hourly to daily timescale and explain 10-30 % locally. On longer timescales (7-20d), only the MJO and equatorial Rossby (ER) waves remain as modulating factors and explain about up to one third of rainfall variability. Eastward inertio-gravity waves and mixed Rossby-gravity (MRG) waves are comparatively unimportant. An analysis of wave superposition shows that low-frequency waves (MJO, ER) in their wet phase amplify the activity of high-frequency waves (TD, MRG) and suppress them in the dry phase. The results stress that more attention should be paid to tropical waves when forecasting rainfall over northern tropical Africa.

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Section: Modulation Intensitymentioning

confidence: 99%

A Systematic Comparison of Tropical Waves over Northern Africa. Part I: Influence on Rainfall

et al. 2019

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“…Although obtaining the KDE function requires more computational time, the PLF results provide valid prediction intervals since the prediction interval coverage probability (PICP) values are in many cases above the prediction interval nominal confidence (PINC), which is 80% [ 53 ]. Furthermore, as Gonzales-Fuentes et al [ 54 ] explained, “What KDE has in its favor is that it requires less samples to provide a good estimate”. This allows obtaining good PLFs in initial states, where the amount of data used to obtain the KDE function is low.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Segarra

Ruiz

Bandera

2021

Sensors

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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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A qualitative study of probability density visualization techniques in measurements

Cited by 2 publications

References 54 publications

A Systematic Comparison of Tropical Waves over Northern Africa. Part I: Influence on Rainfall

A Systematic Comparison of Tropical Waves over Northern Africa. Part I: Influence on Rainfall

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

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