A large-scale study on predicting and contextualizing building energy usage

In this paper we present a data-driven approach to modeling end user energy consumption in residential and commercial buildings. Our model is based upon a data set of monthly electricity and gas bills, collected by a utility over the course of several years, for approximately 6,500 buildings in Cambridge, MA. In addition, we use publicly available tax assessor records and geographical survey information to determine corresponding features for the buildings. Using both parametric and non-parametric learning methods, we learn models that predict distributions over energy usage based upon these features, and use these models to develop two end-user systems. For utilities or authorized institutions (those who may obtain access to the full data) we provide a system that visualizes energy consumption for each building in the city; this allows companies to quickly identify outliers (buildings which use much more energy than expected even after conditioning on the relevant predictors), for instance allowing them to target homes for potential retrofits or tiered pricing schemes. For other end users, we provide an interface for entering their own electricity and gas usage, along with basic information about their home, to determine how their consumption compares to that of similar buildings as predicted by our model. Merely allowing users to contextualize their consumption in this way, relating it to the consumption in similar buildings, can itself produce behavior changes to significantly reduce consumption.National Science Foundation (U.S.) (NSF Computing Innovation Fellowship

Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR

In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP’s purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm)–SVR (Support Vector Regression). The model is applied to a hospital in Korea and 34,636 data sets (1 year) are collected while 31,756 (11 months) sets are used for training and 2880 sets (1 month) are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE), which satisfies the Korea Energy Agency’s and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) error allowance range of ±30%, and ±20% respectively

Energy consumption prediction using people dynamics derived from cellular network data

Energy efficiency is a key challenge for building sustainable societies. Due to growing populations, increasing incomes and the industrialization of developing countries, the world primary energy consumption is expected to increase annually by 1.6%. This scenario raises issues related to the increasing scarcity of natural resources, the accelerating pollution of the environment, and the looming threat of global climate change. In this paper we introduce a new and original approach to predict next week energy consumption based on human dynamics analysis derived out of the anonymized and aggregated telecom data, which is processed from GSM network call data records (CDRs). We introduce an original problem statement, analyze regularities of the source data, provide insight on the original feature extraction method and discuss peculiarities of the regression models applicable for this big data problem. The proposed solution could act on energy producers/distributors as an essential aid to smart meters data for making better decisions in reducing total primary energy consumption by limiting energy production when the demand is not predicted, reducing energy distribution costs by efficient buy-side planning in time and providing insights for peak load planning in geographic space.Telecom Italia SpASET Distribuzione Sp

On the impact of socio-economic factors on power load forecasting

International audienceIn this paper, we analyze a public dataset of electricity consumption collected over 3,800 households for one year and half. We show that some socioeconomic factors are critical indicators to forecast households' daily peak (and total) load. By using a random forests model, we show that the daily load can be predicted accurately at a fine temporal granularity. Differently from many state-of-the-art techniques based on support vector machines, our model allows to derive a set of heuristic rules that are highly interpretable and easy to fuse with human experts domain knowledge. Lastly, we quantify the different importance of each socioeconomic feature in the prediction task

Contextualizing risk and building resilience: returnee versus local entrepreneurs in China

Risk is a pivotal concept in entrepreneurship research, as entrepreneurs constantly face uncertainty, ambiguity, setbacks, and stressful situations. Attitudes toward risk vary contingent upon individual risk preferences and cultural influences. Building resilience is critical for entrepreneurs to overcome obstacles, deal with risk, and grow their ventures. By juxtaposing effectuation theory and resilience literature, we compare the perceptions of risk held by Chinese returnees and local entrepreneurs and their coping strategies in building resilience. Our research reveals two types of coping approaches, namely effectual coping and causal coping. This study contributes to the comparative international entrepreneurship literature by contextualizing the notion of risk held by entrepreneurs influenced by Eastern and Western cultures. Our study further contributes to the nascent literature on resilience in organizations by specifying the entrepreneurial occupational context and exploring the influence of cultures on resilience, and by identifying distinctive resilience‐building coping strategies based upon cultural influences and interpretations of risk. Furthermore, we suggest that resilience can constitute one micro‐foundation of effectuation theory in the context of entrepreneurship dealing with risk

Contextualizing risk and building resilience: returnee versus local entrepreneurs in China

Risk is a pivotal concept in entrepreneurship research, as entrepreneurs constantly face uncertainty, ambiguity, setbacks, and stressful situations. Attitudes toward risk vary contingent upon individual risk preferences and cultural influences. Building resilience is critical for entrepreneurs to overcome obstacles, deal with risk, and grow their ventures. By juxtaposing effectuation theory and resilience literature, we compare the perceptions of risk held by Chinese returnees and local entrepreneurs and their coping strategies in building resilience. Our research reveals two types of coping approaches, namely effectual coping and causal coping. This study contributes to the comparative international entrepreneurship literature by contextualizing the notion of risk held by entrepreneurs influenced by Eastern and Western cultures. Our study further contributes to the nascent literature on resilience in organizations by specifying the entrepreneurial occupational context and exploring the influence of cultures on resilience, and by identifying distinctive resilience‐building coping strategies based upon cultural influences and interpretations of risk. Furthermore, we suggest that resilience can constitute one micro‐foundation of effectuation theory in the context of entrepreneurship dealing with risk

Impact of the surrounding built environment on energy consumption in mixed-use building

While a mixture of residential and non-residential uses in urban development has advantages in reducing transportation energy consumption and improving efficiency of land utilization, the patterns of energy consumption in mixed-use buildings are largely unknown. To understand associations between the built environment and energy consumption and to find effective strategies for energy saving, this study aims to examine how the gas and electricity energy consumption of mixed-use properties is influenced by the characteristics of the immediate surroundings of the building as well as by the building's attributes. The sample for this study is 22,109 mixed-use buildings in Seoul, Korea and the main source of outcome is electricity and gas energy consumption data retrieved from the open system of building data in 2015 and 2016. The regression results showed that a higher proportion of non-residential uses in mixed-use buildings was positively associated with higher electricity consumption overall but that it reduced gas energy use during the winter. In particular, increased restaurant and service use significantly influenced electricity consumption in the buildings. With regard to surrounding built environment, higher impervious surfaces and dense development near the buildings increased the electricity consumption of the buildings but it reduced gas energy consumption. Our results imply that, through the mediating effects of UHIs, the built environment characteristics of immediate surroundings may have indirect effects on energy consumption in mixed-use buildings

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance. Yet, it continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption at larger scales. While we argue that DTs have this transformative potential, they have not yet reached the level of maturity needed to bridge these gaps in a standardized way. Without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. This paper provides a requirement-based roadmap supporting standardized PMx automation using DT technologies. A systematic approach comprising two primary stages is presented. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs to form the backbone of any PMx DT is supported by the track record of IRs and FRs being successfully used as blueprints in other areas, such as for product development within the software industry. Second, we conduct a thorough literature review spanning fields to determine the ways in which these IRs and FRs are currently being used within DTs, enabling us to point to the specific areas where further research is warranted to support the progress and maturation of requirement-based PMx DTs.Comment: (1)This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl