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Although the exposure to PM 2.5 has serious health implications, indoor PM 2.5 monitoring is not a widely applied practice. Regulations on the indoor PM 2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM 2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM 2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM 2.5 was 1.73 μg/m 3 for the LSTM model and in the range of 2.20−4.71 μg/m 3 for the other models when regulatory ambient PM 2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM 2.5 information, the developed models still demonstrated relatively high skill in predicting the PM 2.5 levels in well-mixed indoor air.

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Purpose The purpose of this paper is to identify opportunities, barriers and guidelines for future research in behavioral energy interventions in commercial buildings. Design/methodology/approach The study methodology includes a three-step screening protocol with a collection of prior publications, clustering of related studies and results and analysis of the findings of the prior studies. Findings The review showed that commercial energy interventions were generally successful at impacting occupant energy consumption. Most energy savings were obtained by applying comparative feedback and energy competition strategies, but the lack of long-term effect measurements prevents drawing conclusions regarding their long-term effectiveness. The authors suggest that future studies should explore the impacts that occupant characteristics, environment and community and intervention implementation have on the success of the energy intervention, and integrate these findings into the intervention design. In addition, the authors call for more discussion on the feasibility issues that researchers, policymakers and educators face when implementing these energy interventions to streamline sustainability efforts in the future. Originality/value Research on assessing the effectiveness of occupant behavior interventions has increased considerably over the past decade. This review includes a structured analysis of prior studies of behavioral energy interventions in commercial buildings and encompasses studies conducted between 2005 and 2015. The review is unique in that it focuses on comparing empirical studies that quantified measured energy savings.

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