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As the built environment is among the main contributing sectors to climate change, it is needed to investigate new paradigms to push decarbonization efforts towards the ambitious objectives defined internationally. It is a shared understanding that shifting the perspective from the single building to the district perspective is required to fully take into consideration the complexity of all interactions undergoing within the built environment, thus the concept of Positive Energy District emerged as a district with annual net zero energy import and net zero CO 2 emission working towards an annual local surplus production of renewable energy. In this framework, this paper explores the investigation of the potential for achieving the level of Positive Energy District in a group of non-residential buildings in Balaguer, Catalonia, Spain. These buildings, occupying 8,825 m2 in the city centre, require significant refurbishment for improved energy performance. The analysis includes building energy modelling and simulation, renovation studies, several alternative balance calculations, and carbon emissions assessment. The paper also considers mobility and embodied energy and their impact on energy/carbon balances. The results show that Positive Energy Districts carbon and primary energy balances are not met with rooftop PV installations when retrofitting an existing district towards the Positive energy target but further significant PV areas (roughly + 50%) are required to meet merely the use stage balances: negative results are traced when mobility and embodied energy are computed. A formulation alternative to the simple mathematical balance to facilitate the diffusion of Positive Energy District as catalyst of urban decarbonisation could be needed, including context factors and alternative systems (e.g., rating systems). [ABSTRACT FROM AUTHOR]

[Display omitted] • Incentive initiatives on energy-efficient renovation of existing buildings in China were analyzed. • The dynamic evolution of incentive initiatives was analyzed from different perspectives. • A critical analysis of representative initiatives in two batches of pilot cities was conducted. • Implementation barriers and limitations on energy-efficient renovation of existing buildings were summarized. • Recommendations and pathways of promoting related incentive initiatives were proposed. Under China's national strategy of carbon neutrality by 2060, it is urgently necessary and challenging for the governments to proactively explore policy tools to facilitate energy-efficient renovation of existing buildings. Currently, a considerable number of studies have been conducted on building energy-efficient renovation and its derivative topics, however, a comprehensive overview on incentive initiatives related to existing renovation practices in China is still scarce, such as a lack of critical correlation analysis between national and local initiatives, a lack of the synthesis and critique towards the latest policies and related achievements, and inadequate generalization of the diverse and multi-layered barriers and challenges in building energy-efficient renovation practices. To address these issues, this paper adopts a diversified policy segmentation approach to deeply analyze the dynamic evolution of the incentive initiatives from both national and local level perspectives, as well as to establish the related network of policy linkages between national to local, and between different localities. In addition, this paper presents a critical analysis on representative initiatives in two batches of pilot cities, and proposes good practices and valuable experiences for building energy-efficient renovation. Finally, this paper further summarizes and discusses the barriers to building energy-efficient renovation from four perspectives: governments, householders, enterprises and research institutions, and proposes a series of targeted and feasible pathways and strategies. This study can provide theoretical guidance and targeted recommendations for the formulation of policies, standards and regulations for building energy-efficient renovation in China. [ABSTRACT FROM AUTHOR]

[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper summaries the present regulations and policies concerning M−TES (Mobilized thermal energy storage) in China. • The policies are analyzed based on the aspects including facility manufacture and operation, road transportation, and financial guidance. • The results can provide references for the government or policy makers to promote the development and safe operation of M−TES in China. Mobilized thermal energy storage (M−TES) is a promising technology to transport heat without the limitation of pipelines, therefore suitable for collecting distributed renewable or recovered resources. In particular, the M−TES can be flexibly used for the emergency heating in the COVID-19 era. Though the M−TES has been commercializing in China, there is not any specific regulation or standard for M−TES systems. Therefore, this paper summarizes and discusses the existing regulations and policies concerning M−TES in the aspects of facility manufacture and operation, road transportation, and financial support and guidance. Furthermore, the suggestions were presented including necessary consensus on the development of M−TES among different departments, consideration of local conditions when drafting or revising regulations and policies, sufficient investment, or subsidy on the R&D of M−TES, and qualification recognition of M−TES companies and staffs. [ABSTRACT FROM AUTHOR]

[Display omitted] • High resolution data from a Dutch net zero energy neighborhood is analysed. • Most buildings are positive energy, emit less carbon than they offset. • Large occupant differences in energy demand are visible across the households. • Occupant preferences and weather conditions greatly influence thermal energy demand. • Due to the high thermal efficiency, there is a high risk for summer overheating. Decarbonizing the building stock is a central component of global climate change mitigation efforts. In practice, this decarbonization can be achieved by a variety of different measures, including improvements in building energy efficiency, electrification of energy demand to reduce reliance on fossil fuels, and installation of distributed (renewable) generation in conjunction with flexible storage. However, these large-scale, often disruptive changes to the built environment also raise a number of concerns, such as loss of occupant comfort exacerbated by climate change, and introduction of additional stressors on the distribution grid. In this paper, we demonstrate several conclusions using detailed sub-hourly data of two years (2019–2020) collected from 40 homes in a recently refurbished net-zero energy neighborhood in the Netherlands. This paper shows that, in renovation projects like the case study, net-zero energy balances should be considered on a neighborhood, rather than building level to minimize worst case planning by accounting for occupant influences and seasonal effects. Furthermore, the energy flexibility and climate resilience in the buildings seems to be rather limited, as a result of energy efficiency improvements. While helpful in climate change mitigation efforts, the large seasonal differences in energy demand and generation imply that this evolution is perhaps sub-optimal from the grid perspective. The results illustrate that all homes in the study were net-zero energy over the two year period, sometimes net positive by up to a factor of three. This led to considerable excess generation especially during the summer months. In addition, it was found that indoor air temperature sensors in a number of buildings showed overheating beyond guideline thermal comfort temperature of 25°C, showing potential thermal comfort and heat stress for vulnerable occupants. These results motivate energy storage or modifications of the installed heat pumps to leverage summer excess generation while reducing the impact of summer heat waves. These findings should enable the Netherlands and other countries aiming to fully decarbonize the building stock formulate better, future-proof policies. [ABSTRACT FROM AUTHOR]

• Long term field evaluation of forecast control of heating. • Easy integration with existing installations in buildings. • Widely applicable measure for energy savings around 10%. • Payback time below one heating season. The priority for reaching carbon neutral buildings by 2050 is to first increase energy efficiency in existing buildings by use of easily and widely applicable measures that are verified based on long-term field research. One such solution is a forecast control of heating systems, which can be quickly and easily integrated to existing weather based controls. However, there are still no long-term field evaluation and detailed analyses of the main features of such a system, which would provide a clear picture about the system's energetical and economical features and thus increase their wide application in existing buildings. This paper provides the data from a long term field evaluation of an innovative forecast control of heating systems (forHEAT), which can be easily installed (in less than 2 h) and integrated with existing installations in millions of existing buildings in Europe and throughout the world. The energy consumption for heating was evaluated in seven residential buildings and three public office buildings before and after the installation and use of the forecast control. The resulting energy savings averaged 13.4% in the residential and 10.7% in the public buildings located in continental European climate, with very cold winters. The payback time averaged a 0.6 heating season and in most case studies does not exceed a single heating season. [ABSTRACT FROM AUTHOR]

• A coupled scheme to integrate urban building energy model and physical-based BIPV potential estimation approach. • Demonstrate carbon neutrality assessment workflow for commercial building stock at multiple scales. • Spatiotemporal assessment of energy use intensity, reduction potential and peak loads of commercial building stock. • 84% reduction potential with the simultaneous implementation of all the measures. • Decarbonization potential of BIPV is less than efficiency measures in commercial building stock. The transitional pathway to carbon neutrality in commercial building stock requires improvement of energy efficiency, use of renewable sources and implementation of non-technological mitigation strategies. However, several methods have been developed to assess the carbon neutrality of commercial building stock but they mainly lack the ability of spatiotemporal quantification of energy demand and decarbonization potential which hinders the evaluation of carbon neutrality at multiple scales. This paper presents a GIS-synthetic hybrid urban building energy model (UBEM) coupled with a physical-based approach of building integrated photovoltaics (BIPV) potential estimation to incorporate a building-level energy model at a large scale that could further evaluate the feasibility of carbon neutrality of commercial building stock at a multi-scale level. As a case study, the proposed coupled scheme is applied to the commercial building stock of Tokyo to evaluate the cofound influence of active and passive design measures, and BIPV on the overall decarbonization potential. The results indicate the following findings: the annual CO 2 emissions can be reduced by 84% with the simultaneous implementation of all the measures; focusing on passive design measures is not enough to achieve carbon neutrality in commercial buildings that only contribute 10% of the overall reduction potential; 16% to 40% of the decarbonization potential is contributed by BIPV that is less than efficiency measures; and the scale-bounded comparative analysis shows that total BIPV potential changes in the order of magnitude of 29 to 54 with the variation of scale (with reference to district scale) and 4–16% more self-sufficiency observed at the building-by-building level in comparison to district and city scales. This demonstrates that this coupled approach can provide a purpose-driven perspective for the energy transition of commercial building stock at multiple scales with reduced computational time. Overall, this analysis provides a multi-level perspective to energy modelers and policymakers on how to achieve carbon neutrality in the commercial building stock. [ABSTRACT FROM AUTHOR]

[Display omitted] • Development of two benchmark models for post-world war II residential buildings. • Average energy use intensity per household (archetypes A and B) were 166 and 155 kWh/m2/year. • Models validated with four-year monitoring data on energy consumption. • Households are dominated by senior adults and their occupancy profiles are presented. • Findings on energy needs and use intensity are useful in temperate and continental climates. In the context of the European carbon neutrality targets, building benchmarks are a key issue for the renovation of existing buildings. Although there are various benchmark methods for energy efficiency characterization, their application to the residential sector is still limited. This paper developed two building simulation models for post-world war II houses in Belgium based on data from post-occupancy measurements and field survey campaigns. The study reports the energy characteristics and occupancy profiled of detached single-family houses. An analysis of energy consumption (electricity and natural gas) and a walkthrough survey were conducted between 2016 and 2019. The benchmark model's validity has been further checked against public statistics and verified through model calibration and monthly energy bill comparison. Two reference models representing 633.702 post-WWII single-family houses in Belgium were created and validated. The first archetype has an average energy use intensity of 166 kWh/m2/year and represents detached single-family houses built between 1945 and 1969. The second archetype has an average energy use intensity of 155 kWh/m2/year and represents detached single-family houses built between 1970 and 1990. The paper provides a timely opportunity to evaluate the real performance of post-world war II most common archetypes concerning design assumptions and how building professionals can turn the energy performance gap challenge to their advantage. The findings on energy needs and intensity are useful for creating future renovation scenarios for similar archetypes in Western European countries. [ABSTRACT FROM AUTHOR]

• The paper analyze the drivers of the per-capita CO 2 emissions inequality in building sector across China. • Economic growth helps to narrow regional disparities for residential buildings. • Energy intensity is the primary contributor to emissions inequality. • This paper highlights the importance of per-capita CO 2 emissions in formulating carbon peak planning. There are significant regional differences in CO 2 emissions in China's building sector. Analyzing and understanding this geographic inequality and its drivers is crucial for balanced regional development and greenhouse gas (GHG) emissions reduction. In this paper, we estimated the CO 2 emissions of China's building sector at the provincial level and then calculated the Theil Index of per-capita CO 2 emissions generated from different building types. Based on these results, a Kaya identity was applied to analyze the drivers of the inequality in CO 2 emissions across China. We found that the inequality index of the different types of buildings makes a significant difference. In general, the Theil Index for public buildings and urban buildings is higher than that for rural buildings. Income, energy intensity and energy mix are the principal drivers of regional disparity of per-capita emissions. Economic growth helps to narrow regional disparities for residential buildings, but its contribution is not significant for public buildings. For all building types, energy intensity is the primary contributor to emissions inequality, and the imbalanced implementation of energy efficiency policies is a leading factor. Energy mix plays an increasing role in the regional disparities of per-capita CO 2 emissions, especially for rural buildings. Reducing these regional disparities is crucial to China's goal of achieving peak GHG emissions and reaching carbon neutrality. This paper highlights the importance of per-capita CO 2 emissions in formulating carbon peak planning, allocating regional emissions reduction targets, formulating building energy policies and allocating low-carbon resources. [ABSTRACT FROM AUTHOR]

• A new urban building energy modeling tool-AutoBPS was developed. • AutoBPS calculated urban building energy demands, analyzed energy retrofit and rooftop PV potential via EnergyPlus. • AutoBPS automatically generated multi-zone models, considering shading from surrounding buildings. • A downtown district of 3633 buildings in Changsha, China, was simulated to demonstrate the tool's features. • All three energy conservation measures and PV installation could save 38.6% of total energy demand. China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation. [ABSTRACT FROM AUTHOR]

• A model-free low-carbon management strategy for home energy is proposed. • The proposed strategy accelerates the dynamic operation through deep Q network. • The strategy can achieve the unified effect of economy, emission and satisfaction. • The model adapts to random environment guaranteeing robustness and stability. This paper presents a model-free dynamic optimal management strategy for a low-carbon home energy management system (HEMS) based on deep reinforcement learning (DRL). The method can ideally handle the uncertainties and dynamics of renewable energy and demand-side load. Firstly, the load model is established by a deep Q network (DQN) algorithm with the advantage of ignoring traditional forecasting steps on stochastic environments such as renewable energy generation, load demand, price, etc. Then multi-agents are established for dynamic management based on the DRL. Through "dynamic acquisition, dynamic decision" mechanism, the proposed model-free strategy achieves real-time energy management that can adaptively respond to stochastic environments. Secondly, considering the constraints of system carbon emissions and carbon trading, the proposed strategy can minimize the energy consumption cost, carbon trading cost, and user satisfaction penalties. Ultimately, the effectiveness of the proposed strategy is verified through case studies. Experimental results demonstrate that the strategy can significantly reduce the overall cost, including a 36.7% reduction in carbon trading. At the same time, user satisfaction penalties are reduced by 50.2%. Further, the agent hyperparameter could also be adjusted to capture the trade-off between cost savings and satisfaction penalties. And compared with the traditional forecast-based management strategy, it overcomes the problem of uncertainties and avoids forecasting errors to better accommodate the stochastic environment. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influencing factors on energy efficiency of data center are reviewed. • Energy efficiency metrics are classified according to different priorities. • Appropriate metrics should be selected according to data center function for optimization. • Metrics are analyzed by hierarchy according to the degree of sustainable development. • Challenges and gaps are highlighted and suggestions are given for future works. The rapid development of the Internet requires data center infrastructures to be extended to a large scale with a higher power capacity and a reduction in energy consumption and carbon emissions. It is of great significance for this application to reduce energy consumption, capital cost and carbon emissions while ensuring a safe and reliable data center operation. Evaluation metrics have been considered an effective method to assess the energy efficiency of data centers and have been widely used in various data centers for many years. This work reviews the latest progress of data center evaluation metrics development in recent 20 years. Energy efficiency metrics are summarized based on energy conservation, eco-design and data center security, with the advantages and disadvantages and their correlations discussed. The applicability of each metric is analyzed. The influencing factors of energy efficiency of data centers discussed include site selection, IT equipment, power supply and distribution systems, cooling systems, building structure, and temperature control. The results show that the current evaluation metrics are valuable for the assessment of energy saving of data centers. However, more comprehensive and multi-scale evaluation metrics combining energy consumption, eco-design and data center security are needed to accelerate the progress towards carbon neutrality. In this paper, the hierarchy analysis of metrics is carried out according to sustainability, and metrics in line with sustainable development should be used preferentially. [ABSTRACT FROM AUTHOR]

[Display omitted] • In the US, carbon savings are readily achievable by transitioning to heat pump technologies for the vast majority of homes. • The heat pump performance required to achieve carbon and cost neutrality varies by a factor of four across the US. • Energy cost neutrality is much harder to achieve than carbon neutrality. • Achieving carbon and energy costs savings is much easier when replacing older, low efficiency equipment. In order to meet climate goals, it will be necessary to significantly reduce the greenhouse gases emitted by homes. A key factor in the US is to reduce the on-site combustion of fossil fuels for heating end-uses and to replace this with use of electric heat pump technologies connected to a low-carbon grid. The replacement of natural gas furnaces with electric heat pumps is a key home decarbonization strategy. However, the potential for space heating electrification to reduce greenhouse gas emissions depends on the carbon dioxide equivalent (CO 2 e) content of the electricity used by the heat pump. This varies considerably depending on the source of electricity, with large state to state variability. Furthermore, household energy costs are likely to be impacted by the electrification of space heating, because retail energy prices for both natural gas and electricity in each state vary by factors of seven and four, respectively. Contractors, energy programs, government and building code officials, as well as consumers need clear indications of the likely CO 2 e and energy cost impacts of proposed electrification projects, because these will affect decarbonization choices and rationales around scaled heating electrification. Government and utility programs also need to be aware of the likely outcomes of any supported/incentivized measures. In this paper, we investigate these effects by looking at new metrics to analyze the change in CO 2 e emitted and the cost to meet home heating loads when switching from a natural gas furnace to a heat pump for the contiguous 48 states of the mainland US. [ABSTRACT FROM AUTHOR]

• An innovative retrofit Motivation-Objective-Criteria (MOC) approach integrating homeowners' engagement is proposed in this paper. • Retrofit motivations are categorised into three typical types: 1) Self-living, 2) Rental or sale, and 3) Investment. • A novel Multi-motivation Performance Factor (MPF) concept is proposed to assess the holistic post-retrofit building performance with comprehensive retrofit combinations. • Homeowners' retrofit priorities have an apparent influence on selecting optimised retrofit measures. • This decision-making supporting tool accelerate building retrofit user acceptance and market penetration According to the EU Commission projection (2016/547/EU), the current average renovation rate is far below the expected rate of 3% to achieve carbon neutrality in building sectors by 2050. This is due to the fact that, during the building retrofit optimisation process, the decision-making criteria and objectives are generally optimised separately, and homeowners' motivations are often ignored or not carefully defined in most research. Limitations existed with a lack of in-depth and comprehensive understanding of the homeowners' motivations in undertaking building retrofit. To address this, we propose an inclusive Motivation-Objective-Criteria (MOC) approach, aiming to bring forward optimised decision-making for building renovation, accommodating different homeowners' retrofit motivations, objectives and criteria. Retrofit motivations are categorised into three typical types: 1) Self-living, 2) Rental or sale, and 3) Investment, under five objectives: Energy Reduction Rate (ERR), Initial Investment (II), Discounted Payback Period (DPP), Bills Reduction Rate (BRR) and Carbon Reduction Rate (CRR). A novel Multi-motivation Performance Factor (MPF) concept is proposed to assess the holistic post-retrofit building performance with comprehensive retrofit combinations. A UK semi-detached house is applied as the reference building to investigate the impact of homeowners' engagement on the decision-making of various retrofit measures. According to the cost-optimal results neglecting homeowners' motivation, it is evident that the mismatch of optimal retrofit combinations occurs between the minimum Initial Investment (II) and Discounted Payback Periods (DPP), with optimal ERR of 72%-79% and 82%-93%, respectively. Comparing cost-optimal with multi-objective optimisation results, it is concluded that homeowners' retrofit priorities have an apparent influence on selecting optimised retrofit measures. Besides, party wall insulation is fundamental for self-living- and rental or sale-motivated types. The solar-assisted heat pump and air–water heat pump are not necessary under the retrofit motivation of the "investment" type. Moreover, the attic floor and external wall insulation are imperative for rental or sale- and investment-motivated types. The sensitivity analysis results are conducted in this research, indicating that the optimal retrofit measures and baseline energy consumption input are in good agreement, with a 10% discrepancy. Thus, the proposed inclusive Motivation-Objective-Criteria approach can incorporate homeowners' engagement in the building retrofitting design process, serving as a decision-making supporting tool to accelerate building retrofit with maximised user acceptance and market penetration. [ABSTRACT FROM AUTHOR]

The development of energy-saving technologies for buildings is an important means of achieving carbon neutrality. The respiration-type double-layer glass curtain wall (RDGCW) is a kind of enclosure structure with natural air circulation and a shading function. The RDGCW provides energy saving, and it is being widely promoted and used in China. However, there is still a lack of relevant research on the energy-saving effects and operating strategies of the RDGCW throughout the year. In response, this paper describes experimental evaluation and numerical simulation. First, the energy-saving effects in an office building using an RDGCW in Tianjin during the winter were studied experimentally, and the real-time temperature changes in the RDGCW and the room were measured with high accuracy under different operating strategies. Second, because the temperature distribution and energy consumption in a room is affected by the air flow in the RDGCW and by outdoor meteorological parameters in a complex manner, this investigation developed a coupled model on the basis of TRNSYS and CONTAM. The simulation results were in good agreement with the experimental results. Third, the verified model was used to evaluate the annual energy-saving effects of the RDGCW under dynamic outdoor meteorological conditions in Tianjin. When the optimal operating strategy was adopted, the energy-saving effect of the RDGCW in comparison with single skin façade (SSF) reached 32.8% under summer conditions and 38.3% under winter conditions. Finally, the RDGCW system in typical cities (Changchun, Shanghai, Guangzhou, Kunming and Tianjin) in five climate zones in China was simulated for the whole year. Under the optimal strategy, the RDGCW achieved good energy-saving effects. The energy saving was 27.7–49.2% in summer and 25.6–46% in winter, in comparison with SSF. [ABSTRACT FROM AUTHOR]

• An adaptive power clustering process is proposed to determine the working states of an appliance by analyzing its active power sequences. •.Compared with the traditional HMM-based models, the proposed model ADPC-FHMM could automatically determine the appliances' working states by using the results from the adaptive clustering. •.The proposed method achieves a 10% and 12% improvement in the metric of F-measure on the REDD and AMPds datasets, comparing with the second best algorithm. With the goal of achieving carbon neutrality, the technology of Non-Intrusive Load Monitoring (NILM) has gained widespread attention as an efficient energy-saving way. Hidden Markov Model (HMM) based methods are popular in the field of NILM, yet the traditional HMM-based methods need prior knowledge of the appliance's working states. In this paper, we propose an Adaptive Density Peak Clustering (ADPC) algorithm that could automatically determine the working states of appliance based on its power consumption. Then we combine the ADPC and Factorial Hidden Markov Model (FHMM) to create an Adaptive Density Peak Clustering-Factorial Hidden Markov Model (ADPC-FHMM), which reduces the dependence of prior information and is more applicable in real world scenarios. Case studies are conducted on two publicly available datasets, and the results show that the proposed model outperforms its counterparts on the metrics of Accuracy, F-measure and MAE. [ABSTRACT FROM AUTHOR]