Attention-based interpretable neural network for building cooling load prediction

Li, Ao; Xiao, Fu; Zhang, Chong; Fan, Cheng

doi:10.1016/j.apenergy.2021.117238

Cited by 140 publications

(29 citation statements)

References 18 publications

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“…Ref. [151] proposed a building cooling load prediction model based on attention mechanism and RNN. Attention vectors are used to visualize the impact of the input on the predictions, which helps users understand how the model makes predictions.…”

Section: Power System Flexibilitymentioning

confidence: 99%

Review on Interpretable Machine Learning in Smart Grid

Liao

et al. 2022

Energies

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In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

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Section: Power System Flexibilitymentioning

confidence: 99%

Review on Interpretable Machine Learning in Smart Grid

Liao

et al. 2022

Energies

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“…Recently, with the development of IoT and AI technologies, the building automation systems (BAS) have been transformed into information supported decision making systems. This provides ample opportunities to develop data-driven machine learning (ML) load forecasting models for building operation and control [10,15,17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Current studies on ML models emphasize on new model development under various contexts and application of ML algorithms [11,18,27]. We notice that current ML models commonly report snap-shot accuracy only.…”

Section: Introductionmentioning

confidence: 99%

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Behavior testing of load forecasting models using BuildChecks

Deng

Fan

Jiang

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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In recent years, machine learning (ML) models have been widely developed for building systems. For example, a number of ML models have been developed to predict the load demand of a building. Current ML models commonly report snap-shot accuracy only. Practitioners have difficulties in understanding how a model behaves in usage, i.e., model accuracy may change during model usage. This raises concerns in the ML-model deployment.In this paper, we propose BuildChecks, a behavior testing methodology to systematically evaluate building load forecasting ML models in usage. The challenge of developing such a methodology is to specify "what to evaluate", i.e., given a certain building load forecasting model, what tests we shall apply to this model. We categorize three model-types of the building load forecasting models and we propose three in-usage concerns. Our methodology specifies the tests, i.e., for each model-type, the in-usage concerns that should be tested. We develop an open-source BuildChecks platform to materialize our behavior testing methodology. The BuildChecks platform integrates the testing algorithms and four default realworld building datasets. We use BuildChecks to test the behaviors of two existing load forecasting models. As an example, while a ML model has high accuracy throughout all buildings, BuildChecks reports that in one building this ML-model has a cold start time of 45 days, yet in another building, the cold start time is three-fold greater, 141 days -this can lead to a delay in model usage. CCS CONCEPTS• General and reference → Evaluation.

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“…They are widely used to solve a variety of engineering problems [26]. These include, but are not limited to, the following issues: prognostication of permeability [27,28], gas properties [29,30], wind power prediction [31], building cooling load prediction [32], application to economic issues [33] or issues related to air and land transport [34,35]. The nonlinear characteristics of network neurons have proved very successful in transforming the input data (explanatory variables) to approximate the output value (the dependent variable), which makes them highly effective compared to traditional regression analysis methods, e.g., [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

MLP-Based Model for Estimation of Methane Seam Pressure

2021

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One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the best fit were 2.59% and 3.04% for the average and maximum seam pressure values, respectively. The obtained determination coefficients (exceeding the value of 0.99) confirmed the very good predictive abilities of the models. These results imply a great potential for practical application of the proposed method.

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Attention-based interpretable neural network for building cooling load prediction

Cited by 140 publications

References 18 publications

Review on Interpretable Machine Learning in Smart Grid

Review on Interpretable Machine Learning in Smart Grid

Behavior testing of load forecasting models using BuildChecks

MLP-Based Model for Estimation of Methane Seam Pressure

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