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Review of onsite temperature and solar forecasting models to enable better building design and operations

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Abstract

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. This paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

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Abbreviations

Δh :

forecast horizon

\(\widehat{a}_k^a\) :

ratio of times when ξ (α)t,k equals 1 to overall number of forecasts

δ (β)t,k :

estimated width between the \(\frac{\beta }{2}\) th quantile and \(\left( {1 - \frac{\beta }{2}} \right)\) th quantile at time t for time t + k

ε t :

error terms (noise) at time t

ξ (α) t,k :

indicator function, equals 1 if forecast pt+k is less than \(\widehat{q}_{t + k\left| t \right.}^{\left(a \right)}\)

μ :

mean of the series

AEMET:

State Meteorological Agency (Agencia Estatal de Meteorologia)

AERONET:

Aerosol Robotic Network

AI:

Artificial Intelligence/Intelligent Computer Systems

AIC:

Akaike Information Criterion

AMeDAS:

Automated Meteorological Data Acquisition System by Japan Meteorological Agency, Tokyo, Japan

ANN:

Artificial Neural Network

AR:

Autoregressive

ARIMA:

Autoregressive Integrated Moving Average

ARMA:

Autoregressive Moving-Average

ARMAV:

Multivariable Autoregressive Moving Average Vector System

ARMAX:

Autoregressive Moving Average with Exogenous inputs

ARX:

Autoregressive with Exogenous inputs

ASHARE:

American Society of Heating, Refrigerating and Air-Conditioning Engineers

AVHRR:

Advanced Very High Resolution Radiometer

BIC:

Bayesian Information Criterion

BMA:

Bayesian Model Averaging

BMS:

Building Management System

BN:

Bayesian Network

BOM:

Australian Government Bureau of Meteorology

BSS:

Brier Skill Score

c :

constant value

CBECS:

Commercial Buildings Energy Consumption Survey

CDA:

Conditional Demand Analysis

CDF:

Cumulative Density Function

CFB:

Cascaded Feedforward Backpropagation

CIBSE:

The U.K. Chartered Institution of Building Services Engineers

CRPS:

Continuous Ranked Probability Score

CV:

Coefficient of Variation

CWC:

Coverage Width-based Criterion

DFT:

Discrete Fourier Transform

DMI:

Turkish State Meteorological Service (Turkish: Devlet Meteoroloji Isleri Genel Mudurlugu)

DNI:

Direct Normal Irradiance

DNN:

Deep Neural Network

DRNN:

Deep Recurrent Neural Network

DT:

Decision Tree

ECMWF:

European Centre for Medium-Range Weather Forecasts

EERE:

Office of Energy Efficiency & Renewable Energy

EIA:

Energy Information Administration

ELM:

Elman Backpropagation Neural Network

EM:

Engineering Method

EMPA:

Swiss Federal Laboratories for Materials Testing and Research, Laboratory for Applied Physics in Buildings

ESRA:

European Solar Radiation Atlas

ETS:

Exponential Smoothing

EWMA:

Exponentially Weighted Moving Average

F Y (·):

cumulative density function of response variable Y

FFB:

Feed-Forward Back Propagation

FFNN/FNN/FFN:

Feed Forward Neural Networks

FSS/FS:

Forecast Skill Score

FTDNN:

Focused Time Delay Neural Network

FV3:

Finite-Volume on a Cubed Sphere

GB:

Gradient Boosting

GFS:

Global Forecast System

GHI:

Global Horizontal Irradiance

GHIt,Measured :

Measured Global Horizontal Irradiance observation at time t

GHIt,ClearSky :

Global Horizontal Irradiance generated from Clear Sky Model at time t

GHIt,Ex :

extraterrestrial solar irradiance at time t

GMA:

Geostatistical Model Averaging

GP:

Gaussian Process

GVI:

Global Vertical Irradiance

HMS:

Hungarian Meteorological Service

HUST:

Huazhong University of Science and Technology, Wuhan, China

INMET:

Brazilian National Institute of Meteorology (Instituto Nacional de Meteorologia)

IWEC:

International Weather for Energy Calculations

k :

clear sky index (can also be clearness index)

\(\widehat{k}_{t,{\text{ClearSkyIndex}}}\) :

predicted clear sky index at time t

\(\widehat{k}_{t,{\text{ClearnessIndex}}}\) :

predicted clearness index at time t

\({\bar k_{t - \Delta h,t}}\) :

point forecasted clear sky index averaged in the window between time t-Δh and time t

KACARE:

King Abdullah City for Atomic and Renewable Energy

KDC KNMI:

(Koninklijk Nederlands Meteorologisch Instituut) Data Centre

KF:

Kalman Filter

KMA:

Korea Meteorological Administration

k-NN k-nearest:

neighbors

LASSO:

Least Absolute Shrinkage and Selection Operator

LSTM:

Long Short-Term Memory

MA:

Moving Average

MAE:

Mean Absolute Error

MAPE:

Mean Absolute Percentage Error

MAXAE:

Maximum Absolute Error

MBE:

Mean Bias Error

MeteoSwiss:

The Federal Office of Meteorology and Climatology

MIDC:

Measurement and Instrumentation Data Center

MLP:

Multilayer Perceptron

MNRE:

Indian Ministry of Renewable Energy

MOGA:

Multi-objective Genetic Algorithm

MPC:

Model Predictive Control

MSE:

Mean Square Error

n (α)k,1 :

times when ξ (α)t,k equals 1

N :

number of sample data (sample size)

NAR:

Nonlinear Autoregressive Neural Network

NARR:

North American Regional Reanalysis Data

NARX:

Nonlinear Autoregressive Neural Network with Exogenous inputs

NASA:

National Aeronautics and Space Administration

NCEP:

National Centre for Environmental Prediction

NDFD:

National Digital Forecast Database

nMAE:

Normalized Mean Absolute Error

nMBE:

Normalized Mean Bias Error

nMSE:

Normalized Mean Square Error

NN:

Neural Networks

NOAA:

National Oceanic and Atmospheric Administration

NRCAN:

Natural Resources Canada

NREL:

National Renewable Energy Laboratory

nRMSE:

Normalized Root Mean Square Error

NWP:

Numerical Weather Prediction

OLS:

Ordinary Least Square

ORNL:

Oak Ridge National Laboratory

p :

data series order of autoregressive component

p t :

forecast at time t

P :

probability function

PDF:

Probability Density Function

PI:

Prediction Intervals

PICP:

Prediction Intervals Coverage Probability

PINAW:

Prediction Intervals Normalized Average Width

PNN:

Probabilistic Neural Network

PV:

photovoltaic

Q Y (·):

quantile function of Y response variable

q :

data series order of moving-average component

\(\widehat{q}_{t + k\left| t \right.}^{\left(a \right)}\) :

empirical αth quantile estimation for time t+k at times t

RBFN:

Radial Basis Function Network

RBFNN:

Radial Basis Feed-forward Neural Network

RBN:

Radial Basis Neural Network

RERC:

Renewable Energy Center

RF:

Random Forests

RMSE:

Root Mean Square Error

RNN:

Recurrent Neural Network

RSR:

Rotating Shadow-band Radiometer

SARIMA:

Seasonal Autoregressive Integrated Moving Average

SDE:

Standard Deviation of Errors

SERIS:

Solar Energy Research Institute of Singapore

SMHI:

Swedish Meteorological and Hydrological Institute

SPDIS:

Solar Power Data for Integration Studies

SPMA:

Simple Prior Moving Average

SS:

State Space

SURFRAD:

Surface Radiation Network

SVM:

Support Vector Machine

SVM-C:

Support Vector Machine-Classification

SVR/SVM-R:

Support Vector Machine-Regression

t :

time

TDNN:

Time Delay Neural Network

WF:

Linear Weighted Forecasting

WRF:

Weather Research and Forecasting

X it :

the ith predictor variable at time t

\({\widehat{Y}_{t + {\rm{\Delta}}h}}\) :

forecasted response variable at time t+Δh

Y t :

actual response variable (e.g. GHI (W·m−2), temperature (K/°C)) at time t

Y t+Δh :

response variable at time t+Δh

\(\bar Y \) :

mean of actual response variable data

\(\bar {\widehat{Y}}\) :

mean of forecasted response variable data

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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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Authors and Affiliations

  1. Department of Mechanical & Aerospace Engineering, Syracuse University, Syracuse, NY, 13244, USA

    Bing Dong

  2. Department of Management Science & Statistics, University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Reisa Widjaja & Wenbo Wu

  3. Energy Systems Division, Argonne National Laboratory, Lemont, IL, 60439, USA

    Zhi Zhou

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  1. Bing Dong
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  2. Reisa Widjaja
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  3. Wenbo Wu
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  4. Zhi Zhou
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Correspondence to Zhi Zhou.

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Dong, B., Widjaja, R., Wu, W. et al. Review of onsite temperature and solar forecasting models to enable better building design and operations. Build. Simul. 14, 885–907 (2021). https://doi.org/10.1007/s12273-020-0759-2

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  • DOI: https://doi.org/10.1007/s12273-020-0759-2

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