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The paper aims to develop a particle matter (PM_2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM_2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM_2.5 require either time-consuming or expensive equipment, thus making PM_2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.

This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.

The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.

This work demonstrates that by combining low-cost sensors and ML, indoor PM_2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.

The purpose of this paper is to identify opportunities, barriers and guidelines for future research in behavioral energy interventions in commercial buildings.

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.

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.

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.