Abstract
Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. We demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model’s performance in different landforms and climate types.
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Abbreviations
- d(s i, s j):
-
geo-distance between locations si and Sj F forecast horizon
- L :
-
temporal lags of model input features
- N t :
-
total number of available PWSs with complete data at time t
- p :
-
total number of input weather variables (k=1, 2, p)
- R :
-
radius (number of stations) about a target location
- s i :
-
location of station i, e.g., geo-coordinates
- t :
-
specific time-tick
- W :
-
rolling window size
- X k(s i, t):
-
the k-th input weather variable for locations si at time t
- X si,t = X 1(s i,t),…,X p(s i,t):
-
all the input weather variables of stations si at time t
- Y si,t :
-
target weather variable of interest for locations si at time t
- δ lat(s i, s j):
-
difference in latitudes between locations si and sj
- δ lon(s i, s j):
-
difference in longitude between locations si and sj
- ε t :
-
error terms (noise) at time t
References
Aryaputera AW, Yang D, Walsh WM (2015). Day-ahead solar irradiance forecasting in a tropical environment. Journal of Solar Energy Engineering, 137: 051009.
Baran S, Horányi A, Nemoda D (2014). Probabilistic temperature forecasting with statistical calibration in Hungary. Meteorology and Atmospheric Physics, 124: 129–142.
Bengea SC, Kelman AD, Borrelli F, et al. (2014). Implementation of model predictive control for an HVAC system in a mid-size commercial building. HVAC&R Research, 20: 121–135.
Berk RA (2008). Statistical Learning from a Regression Perspective. New York: Springer.
Breiman L (2001). Random forests. Machine Learning, 45: 5–32.
Bursill J, O’Brien W, Beausoleil-Morrison I (2019). Experimental application of classification learning to generate simplified model predictive controls for a shared office heating system. Science and Technology for the Built Environment, 25: 615–628.
Chen T-Y, Athienitis AK (1996). Ambient temperature and solar radiation prediction for predictive control of HVAC systems and a methodology for optimal building heating dynamic operation. ASHRAE Transactions, 102(1): 26–35.
De Coninck R, Helsen L (2016). Practical implementation and evaluation of model predictive control for an office building in Brussels. Energy and Buildings, 111: 290–298.
Dong B, Lam KP (2014). A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Building Simulation, 7: 89–106.
Dong B, Widjaja R, Wu W, et al. (2021). Review of onsite temperature and solar forecasting models to enable better building design and operations. Building Simulation, 14: 885–907.
Dovrtel K, Medved S (2012). Multi-objective optimization of a building free cooling system, based on weather prediction. Energy and Buildings, 52: 99–106.
Ferreira PM, Gomes JM, Martins IAC, et al. (2012). A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature. Sensors, 12: 15750–15777. [PubMed]
Florita AR, Henze GP (2009). Comparison of short-term weather forecasting models for model predictive control. HVAC&R Research, 15: 835–853.
Friedman JH (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38: 367–378.
Gneiting T, Raftery AE, Westveld AHIII, et al. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133: 1098–1118.
Henze GP, Felsmann C, Knabe G (2004). Evaluation of optimal control for active and passive building thermal storage. International Journal of Thermal Sciences, 43: 173–183.
Huang H, Chen L, Mohammadzaheri M, et al. (2013). Multi-zone temperature prediction in a commercial building using artificial neural network model. In: Proceedings of the 10th IEEE International Conference on Control and Automation (ICCA), Hangzhou, China.
Kleiber W, Raftery AE, Baars J, et al. (2011). Locally calibrated probabilistic temperature forecasting using geostatistical model averaging and local Bayesian model averaging. Monthly Weather Review, 139: 2630–2649.
Lanza PAG, Cosme JMZ (2001). A short-term temperature forecaster based on a novel radial basis functions neural network. International Journal of Neural Systems, 11: 71–77.
Lazos D, Sproul AB, Kay M (2015). Development of hybrid numerical and statistical short term horizon weather prediction models for building energy management optimisation. Building and Environment, 90: 82–95.
Lazos D, Kay M, Sproul A (2016). Development of a numerical weather analysis tool for assessing the precooling potential at any location. Energies, 10: 21.
Lima FJL, Martins FR, Pereira EB, et al. (2016). Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks. Renewable Energy, 87: 807–818.
Loh WY (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1: 14–23.
Möller A, Groý J (2020). Probabilistic temperature forecasting with a heteroscedastic autoregressive ensemble postprocessing model. Quarterly Journal of the Royal Meteorological Society, 146: 211–224.
Nagai T (2007). A method for revising temperature and humidity prediction using additional observations and weather forecasts. In: Proceedings of the 10th International IBPSA Building Simulation Conference, Beijing, China.
Oldewurtel F, Parisio A, Jones CN, et al. (2010). Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions. In: Proceedings of the 2010 American Control Conference, Baltimore, MD, USA.
Oldewurtel F, Parisio A, Jones CN, et al. (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45: 15–27.
Peng Y, Rysanek A, Nagy Z, et al (2016). Case study review: Prediction techniques in intelligent HVAC control systems. In: Proceedings of 9th International Conference on Indoor Air Quality Ventilation and Energy Conservation in Buildings (IAQVEC 2016), Songdo, R.O. Korea.
Raftery AE, Gneiting T, Balabdaoui F, et al. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133: 1155–1174.
Ren MJ, Wright JA (2002). Adaptive diurnal prediction of ambient dry-bulb temperature and solar radiation. HVAC&R Research, 8: 383–401.
Seem JE (1991). Adaptive methods for realtime forecasting of building electrical demand. ASHRAE Transactions, 97(1): 710–721.
Yang C, Xie L (2012). A novel ARX-based multi-scale spatio-temporal solar power forecast model. In: Proceedings of 2012 North American Power Symposium (NAPS), Champaign, IL, USA.
Yoshida H, Terai T (1991). An ARMA type weather model for air-conditioning, heating and cooling load calculation. Energy and Buildings, 16: 625–634.
Zavala VM, Constantinescu EM, Krause T, et al. (2009). On-line economic optimization of energy systems using weather forecast information. Journal of Process Control, 19: 1725–1736.
Zhang Y, Hanby VI (2007). Short-term prediction of weather parameters using online weather forecasts. In: Proceedings of the 10th International IBPSA Building Simulation Conference, Beijing, China.
Zhang R, Yang H (2015). Dynamic building energy consumption forecast using weather forecast interpolations. In: Proceedings of 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), Miami, FL, USA.
Acknowledgements
This work was supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy through its Building Technologies Office. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE AC02-06CH11357. The views expressed in this article are the authors’ own and do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
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Widjaja, R.F., Wu, W., Zhou, Z. et al. A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data. Build. Simul. 16, 963–982 (2023). https://doi.org/10.1007/s12273-022-0974-0
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DOI: https://doi.org/10.1007/s12273-022-0974-0