Generating High Resolution Near-Future Weather Forecasts for Urban Scale Building Performance Modelling

Du, Hu; Barclay, Michael; Jones, Phillip John

doi:10.26868/25222708.2017.234

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…This will be accelerated by the increasing number of weather forecasting tools through Application Programming Interfaces (APIs) that provide point location weather forecast data in various programmable formats [107]. The FAO Penman-Monteith equation for the estimation of ET using real-time or forecast weather API data can be combined with continuous soil water content monitoring data from soil sensors [32] as applied in the WIDSS architecture.…”

Section: Lessons Learned and Future Perspectivesmentioning

confidence: 99%

“…These online weather data sources use atmospheric models in predicting the weather. They include Aemet (7-day forecast), metoffice (5-day forecast), met.no (10-day forecast), openweathermap (5-day forecast), weatherbit (5-day forecast), darksky (7-day forecast), wunderground (10-day forecast) and apixu (10-day forecast) [14]. They are differentiated by the degree of accuracy and the length of the forecast horizon.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Mubangizi¹,

Wanyama²,

Kiggundu³

et al. 2023

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Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts; to know "how much water to apply" and "when to irrigate", which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.

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Section: Lessons Learned and Future Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Mubangizi¹,

Wanyama²,

Kiggundu³

et al. 2023

View full text Add to dashboard Cite

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Nowcasting Methods For Optimising Building Performance

Du¹,

Bandera²,

Chen³

Building Simulation Conference Proceedings

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In meteorology term, nowcasting is weather forecasting for the next few minutes to six hours using all immediately available weather data. It is a relatively new subject, which often involves remote sensing, numerical weather prediction models, and advanced data communication infrastructure. High-quality weather nowcasting is crucial for optimising building performance in the near future. A range of nowcasting techniques has been used for such purposes. It includes statistical, machine learning, Numerical Weather Prediction (NWP), top-down and bottom-up approaches. This paper firstly reviews the advantages and disadvantages of common nowcasting methods with the focus on solar radiation nowcasting. Based on the review, popular methods have been classified into five categories. Authors then investigated further the nowcasting data provided by weather Application Programming Interfaces (APIs) that is backed by Numerical Weather Prediction. This is due to its large-scale application potential and the significances in the most recent update on solar radiation nowcast. Secondly, the paper explores the implications of applying weather nowcasting to dynamic building simulations, most importantly, examining its impact on the accuracy of indoor temperature prediction for free float buildings, heating load prediction and heating energy for heated buildings. The study used three buildings from BESTEST ANSI/ASHRAE Standard 140-2014 as the case studies. The results show that the most recent update of weather API includes meaningful solar radiation prediction. If the building does not have a large south facing glazing, the indoor temperature and heating load predictions from dynamic models are reasonably accurate.

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Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Segarra

Ruiz

et al. 2019

Energies

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The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

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Generating High Resolution Near-Future Weather Forecasts for Urban Scale Building Performance Modelling

Cited by 4 publications

References 12 publications

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Nowcasting Methods For Optimising Building Performance

Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

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