Embedded On-line System for Electrical Energy Measurement and Forecasting in Buildings

Nichiforov, Cristina; Stâmâtescu, Grigore; Stamatescu, Iulia; Arghira, Nicoleta; Făgărăşan, Ioana; Iliescu, Sergiu Stelian

doi:10.1109/idaacs.2019.8924350

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…The evaluation of the model's accuracy is performed by using several metrics widely applied in the literature [56][57][58]. Additionally, to the MSE index, which is previously defined, the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) were used as well.…”

Section: Convolutional Neural Network (Cnn) Training and Performance Evaluationmentioning

confidence: 99%

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

et al. 2021

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Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

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Section: Convolutional Neural Network (Cnn) Training and Performance Evaluationmentioning

confidence: 99%

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

et al. 2021

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show abstract

“…A recent text mining-driven review of 30,000 building energyrelated data science publications shows that there has been rapid growth in the last ten years in techniques and applications [13]. This rapid expansion of the field is creating a myriad of prominent techniques using deep learning [14,7,15], embedded online systems [16], sequence learning [17], transfer learning [18,19], neural networks [20,18], and gradient boosting trees [21,22].…”

Section: Introductionmentioning

confidence: 99%

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Miller,

Picchetti,

et al. 2021

Preprint

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Machine learning for building energy prediction has exploded in popularity in recent years, yet understanding its limitations and potential for improvement are lacking. The ASHRAE Great Energy Predictor III (GEPIII) Kaggle competition was the largest building energy meter machine learning competition ever held with 4,370 participants who submitted 39,403 predictions. The test data set included two years of hourly electricity, hot water, chilled water, and steam readings from 2,380 meters in 1,448 buildings at 16 locations. This paper analyzes the various sources and types of residual model error from an aggregation of the competition's top 50 solutions. This analysis reveals the limitations for machine learning using the standard model inputs of historical meter, weather, and basic building metadata. The types of error are classified according to the amount of time errors occur in each instance, abrupt versus gradual behavior, the magnitude of error, and whether the error existed on single buildings or several buildings at once from a single location. The results show machine learning models have errors within a range of acceptability (RMSLE scaled =< 0.1) on 79.1% of the test data. Lower magnitude model errors (0.1 < RMSLE scaled =< 0.3) occur in 16.1% of the test data. These discrepancies can likely be addressed through additional training data sources or innovations in machine learning. Higher magnitude errors (RMSLE scaled > 0.3) occur in 4.8% of the test data and are unlikely to be accurately predicted regardless of innovation. There is a diversity of error behavior depending on the energy meter type (electricity prediction models have unacceptable error in under 10% of test data, while hot water is over 60%) and building use type (public service less than 14%, while technology/science is just over 46%). Most of the instances of continuous error last longer than three days, and significant portions come from individual buildings and collections of buildings from the same site. This analysis forms the foundation for suggestions to reduce machine learning errors by collecting and using additional training data from onsite and web-based sources to improve the capability, accuracy, scalability, and usability of machine learning. Improvement of these metrics could enhance greater adoption in built environment applications.

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Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Miller

Picchetti

et al. 2022

Science and Technology for the Built Environment

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Embedded On-line System for Electrical Energy Measurement and Forecasting in Buildings

Cited by 6 publications

References 14 publications

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

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