Can I read Behavior testing of load forecasting models using BuildChecks on EtoBox?

Behavior testing of load forecasting models using BuildChecks by Yang Deng; Jiaqi Fan; Hao Jiang; Fang He; Dan Wang; Ao Li; Fu Xiao is a scholarly article available to read on EtoBox.

What is Behavior testing of load forecasting models using BuildChecks about?

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 t

Author

Yang Deng; Jiaqi Fan; Hao Jiang; Fang He; Dan Wang; Ao Li; Fu Xiao

Publisher

ACM

Published

2022

Language

EN