A review and analysis of regression and machine learning models on commercial building electricity load forecasting

Yildiz, Baran; Bilbao, José Ignacio; Sproul, Alistair B.

doi:10.1016/j.rser.2017.02.023

Cited by 421 publications

(209 citation statements)

References 44 publications

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“…Commonly used metrics to evaluate forecast accuracy are the coefficient of variation (CV RMSE), the root mean squared error (RMSE) and the MAE [40]. CV (RMSE) is the RMSE normalized by the mean of the measured values and quantifies typical size of the error relative to the mean of the observations.…”

Section: Performance Metrics For Evaluationmentioning

confidence: 99%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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Abstract:Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan's electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.

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Section: Performance Metrics For Evaluationmentioning

confidence: 99%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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“…These models eliminate the need for extensive prior knowledge about the buildings and users; however, they often require a large amount of training data for each building of interest [34][35][36]39] or result in insufficient prediction accuracy, especially for long-term forecasting (1-5 day ahead forecasting) [37,40,43,44]. Among many, neural networks have been widely used in load forecasting [18,19,29,34,36,37] to obtain accurate predictions of building loads. Neural Network models take different inputs such as environmental parameters, occupancy information, inputs from the sensors on the HVAC system etc.…”

Section: Related Workmentioning

confidence: 99%

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Prakash

Rajagopal

et al. 2018

Energies

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Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short-and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).

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“…The transient terms of each governing equation have been written in terms of the driving potentials to take more advantage of the Multitridiagonal-Matrix Algorithm (MTDMA) solution algorithm [39]. classification [30][31][32] and nonlinear regression [33][34][35] areas, mapping the input data in a space with characteristics of high dimensionality. A variation of SVMs, known as LS-SVMs (Least Squares Support Vector Machines), adopted in this work, was proposed, evaluated, and compared to the classical version of SVMs [36,37] for a regression/identification task.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…SVM refers to a kernel-based method, similar to artificial neural network (ANN) models, which constitute an approximate implementation of the structural risk minimization principle [29]. Considering structures called nuclei (kernels), SVMs go beyond the hyperplanes generated initially, been widely applied in classification [30][31][32] and nonlinear regression [33][34][35] areas, mapping the input data in a space with characteristics of high dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines

2017

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Abstract:The hygrothermal analysis of roofs is relevant due to the large areas exposed to a wide range of weather conditions, these directly affecting the energy performance and thermal comfort of buildings. However, after a long life service, the solar absorptivity coatings of roofs can be altered by mould accumulation. Based on two well established mathematical models, one that adopts driving potentials to calculate temperature, moist air pressure and water vapor pressure gradients, and the other to estimate the mould growth risk on surfaces, this research introduces an approach to predict mould growth considering a reduced computational effort and simulation time. By adopting multiple MISO (Multiple-Input, Single-Output) Nonlinear AutoRegressive with eXogenous inputs (NARX) models, a machine learning technique known as Least Squares Support Vector Machines (LS-SVM), a maximum margin model based on structural risk minimization, was used to predict vapor flux, sensible heat flux, latent heat flux, and mould growth risk on roof surfaces. The proposed model was validated in terms of the Multiple Correlation Coefficient (R 2 ), Mean Square Error (MSE) and Mean Absolute Error (MAE) performance indices considering as input the weather file from Curitiba city-Brazil, showing consistent precision when compared to the results of a validated numerical model.

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A review and analysis of regression and machine learning models on commercial building electricity load forecasting

Cited by 421 publications

References 44 publications

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Hygrothermal Dynamic and Mould Growth Risk Predictions for Concrete Tiles by Using Least Squares Support Vector Machines

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