Mengjie Han

Dalarna University · micro-data analysis

Location Models are used to locate n service centers in order to serve a geographically distributed population. A cornerstone of such models is the measure of distance between the service center and a set of demand points, viz, the location of the population (customers, pupils, patients and so on). Evidence support the current practice of using the...

This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse to what extent local population change is affected by neighbouring populations. To do this, we focused on...

The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population patronize the nearest facility and that the distance between the resident and the facility may be measured by the Euclidean distance. Carling, Han, and Håkansson (2012) compared two network distances...

Learning effectiveness evaluation requires the integration of objective test results and the analysis of uncertain subjective evaluations. The methods of the fuzzy theory are applicable to handle the fuzzy information and uncertainty for obtaining more comprehensive and accurate evaluation results. Due to its deflation ability and the representatio...

Positive energy districts (PEDs) are urban areas which seek to take an integral approach to climate neutrality by including technological, spatial, regulatory, financial, legal, social, and economic perspectives. It is still a new concept and approach for many stakeholders. ChatGPT, a generative pre-trained transformer, is an advanced artificial in...

It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of th...

Electric vehicle (EV) smart charging can effectively enhance the local power demand-supply balance and help improve the renewable power local utilization. Researchers have developed two categories of EV charging: (i) building/grid-to-vehicle, in which the EV can only be charged; and (ii) building/grid-to-vehicle-to-building/grid, in which the vehic...

We combine current literature in software quality metrics with an attitude validation study with industry practitioners, to establish how quality metrics can be used for data-driven approaches. We also propose a simple metric nomenclature and map our findings into a decision making model for easy adoption and utilization of data-driven decision-mak...

Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community’s self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various...

Considering the situation where decision values are \(q\)-rung orthopair triangular fuzzy number (\(q\)-ROTFN) and pair-wise comparisons of alternatives and evaluation matrices are given by decision-makers, a new group decision-making method is necessary to be studied for solving a group decision-making problem in the above situation. In this paper...

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease’s spread, although control measures have been put in pla...

Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expens...

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease’s spread, although control measures have been put in pla...

Building ventilation system needs to be controlled in a smart way to maintain indoor air quality while reducing energy use. Although many demand-controlled methods have been developed, the design of ventilation schedule has to be customized depending on local climate, occupant behavior and system capacity. This article introduces an easy-to-use con...

The slogan "Heat is half" is of importance to keep in mind that nearly 50 % of the final energy use is in the form of heat. The global efforts for future decarbonised heating systems are based on hydrogen and electrification of heating etc. Solar thermal technology is a key component of greener industrial heating solutions. Solar thermal technologi...

The Predicted Mean Vote (PMV) model is extensively used by current thermal comfort standards, such as ASHRAE 55 and ISO 7730, despite its discrepancy in predicting Thermal Sensation (TS). The implicit assumption is that PMV can be applied for predicting TS of a large population. Our statistical analysis of a subset of ASHRAE global database of ther...

The energy shortage is one key issue for sustainable development, a potential solution of which is the integration with the renewable energy resources. However, the temporal sequential characteristic of renewable resources is different from traditional power grid. For the entire power grid, it is essential to match the energy generation side with t...

An occupant’s window opening and closing behaviour can significantly influence the level of comfort in the indoor environment. Such behaviour is, however, complex to predict and control conventionally. This chapter, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control...

Urban energy mapping plays a crucial role in benchmarking the energy performance of buildings for many stakeholders. This study examined a set of buildings in the city of Borlänge, Sweden, owned by the municipality. The aim was to present a digital spatial mapping of both electricity use and district heating demand. A toolkit for top-down data proc...

The lighting system accounts for 8% of the total electricity consumption in commercial buildings in the United States and 12% of the total electricity consumption in public buildings globally. This consumption share can be effectively reduced using the demand-response control. The traditional lighting system control method commonly depends on the r...

Classical building control systems are becoming vulnerable with increasing complexities in contemporary built environments and energy systems. Due to this, the reinforcement learning (RL) method is becoming more distinctive and applicable in control networks for buildings. This chapter, therefore, conducts a comprehensive review of RL techniques ap...

Traditional occupant behavior modeling has been studied at the building level, and it has become an important factor in the investigation of building energy consumption. However, studies modeling occupant behaviors at the urban scale are still limited. Recent work has revealed that urban big data can enable occupant behavior modeling at the urban s...

A digital twin is regarded as a potential solution to optimize positive energy districts (PED). This paper presents a compact review about digital twins for PED from aspects of concepts, working principles, tools/platforms, and applications, in order to address the issues of both how a digital PED twin is made and what tools can be used for a digit...

Urban energy mapping plays a crucial role in benchmarking the energy performance of buildings for many stakeholders. This study examined a set of buildings in the city of Borlänge, Sweden, owned by the municipality. The aim was to present a digital spatial map of both electricity use and district heating demand in the spatial-temporal dimension. A...

In recent years, a building's energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building's performance levels. Assessing energy performance of buildings at a...

The identification of underground geohazards is always a difficult issue in the field of underground public safety. This study proposes an interactive visualization framework for underground geohazard recognition on urban roads, which constructs a whole recognition workflow by incorporating data collection, preprocessing, modeling, rendering and an...

Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling...

Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dyn...

An occupant's window opening and closing behaviour can significantly influence the level of comfort in the indoor environment. Such behaviour is, however, complex to predict and control conventionally. This paper, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control ai...

The lighting system accounts for 8% of the total electricity consumption in commercial buildings in the United States and 12% of the total electricity consumption in public buildings globally. This consumption share can be effectively reduced using the demand-response control. The traditional lighting system control method commonly depends on the r...

In the last four decades several methods have been used to model occupants’ presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents...

The Predicted Mean Vote (PMV) model is extensively used by current thermal comfort standards, such as ASHRAE 55 and ISO 7730, despite its discrepancy in predicting Thermal Sensation (TS). The implicit assumption is that PMV can be applied for predicting TS of a large population. Our statistical analysis of a subset of ASHRAE global database of ther...

The energy shortage is one key issue for sustainable development, a potential solution of which is the integration with the renewable energy resources. However, the temporal sequential characteristic of renewable resources is different from traditional power grid. For the entire power grid, it is essential to match the energy generation side with t...

Building control systems are prone to fail in complex and dynamic environments. The reinforcement learning (RL) method is becoming more and more attractive in automatic control. The success of the reinforcement learning method in many artificial intelligence applications has resulted in an open question on how to implement the method in building co...

The solar energy share in Sweden will soar over the next decades. Such transition offers not great opportunity but uncertainties for the emerging solar PV/thermal (PV/T) technologies. There are still critical challenges for PV/T development in Swedish scenario. This paper aims to investigate the local policies and market structures in Sweden by a s...

Classical building control systems are becoming vulnerable with increasing complexities in contemporary built environments and energy systems. Due to this, the reinforcement learning (RL) method is becoming more distinctive and applicable in control networks for buildings. This paper, therefore, conducts a comprehensive review of RL techniques appl...

The aim of this paper is to analyse labour turnover in retail firms with stores in different city locations. This case study of a Swedish mid-sized city uses comprehensive longitudinal register data on individuals. In a first step, an unconditional descriptive analysis shows that labour turnover in retail is higher in out-of-town locations, compare...

The quality of a heuristic solution to a NP-hard combinatorial problem is hard to assess. A few studies have advocated and tested statistical bounds as a method for assessment. These studies indicate that statistical bounds are superior to the more widely known and used deterministic bounds. However, the previous studies have been limited to a few...

The emergence of renewable-energy-source (RES) envelope solutions, building retrofit requirements and advanced energy technologies brought about challenges to the existing paradigm of urban energy systems. It is envisioned that the building cluster approach—that can maximize the synergies of RES harvesting, building performance, and distributed ene...

Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but a...

This paper empirically measures the potential spillover effects of big-box retail entry on the productivity of incumbent retailers in the entry regions, and investigates whether the effects differ depending on 1) if the entry is in a rural or urban area, and 2) if the incumbent retailers are within retail industries selling substitute or complement...

A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, includin...

Regarding the location of a facility, the presumption in the widely used p-median model is that the customer opts for the shortest route to the nearest facility. However, this assumption is problematic on free markets since the customer is presumed to gravitate to a facility by the distance to and the attractiveness of it. The recently introduced g...

We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on th...

The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. The first aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to find an e...

Denna artikel visar på en metod för att undersöka hur optimal befolkningens fysiska tillgänglighet till sjukvården är. Detta är relevant med tanke på den svenska storregionala omdaningen som säkerligen kommer provocera fram omprövningar av sjukhusens framtida placering. Med Dalarna som exempel fann vi att en ökning från dagens två till tre optimalt...

In this article we use a method that is handy when investigating how optimal a population's physical access to hospitals is. In Sweden this is a relevant question since regional restructuring most certainly will affect where the future hospitals will be located. By using the region of Dalarna as an example we find that by increasing the present two...

A customer is presumed to gravitate to a facility by the distance to it and the attractiveness of it. However regarding the location of the facility, the presumption is that the customer opts for the shortest route to the nearest facility.This paradox was recently solved by the introduction of the gravity p-median model. The model is yet to be impl...

The p-median model is used to locate P centers to serve a geographically distributed population. A cornerstone of such a model is the measure of distance between a service center and demand points, i.e. the location of the population (customers, pupils, patients, and so on). Evidence supports the current practice of using Euclidean distance. Howeve...

The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. Distance measure can vary if the accuracy of the road network varies. The first aim in this study is to analyze how the optimal location solutions vary, using the p-medi...

Location Models are used for planning the location of multiple service centers in order to serve a geographically distributed population. A cornerstone of such models is the measure of distance between the service center and a set of demand points, viz, the location of the population (customers, pupils, patients and so on). Theoretical as well as e...