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Effective energy management from the demand side and smart meters play an important role in achieving carbon neutrality. The government and utilities in South Korea are working to expand the installation of smart meters and develop time-of-use (TOU) tariffs for residential customers. Although these efforts have expanded the selection of tariffs for customers, it has become increasingly difficult to determine electricity tariffs. Therefore, some studies have been conducted to recommend tariffs for residential customers with historical metering data. However, recommending tariffs for customers who have recently installed smart meters or have failed to obtain historical metering data is a challenging task. Therefore, this paper presents a systematic method to estimate energy consumption patterns and incorporate behavioral changes based on the input profiles of residential customers for personalized electricity tariff recommendations. The proposed method attempts to predict bills by estimating energy consumption patterns using customer profiles. It is designed to reflect the behavioral changes in each pattern caused by the TOU tariff in predicting bills. In addition, the bill prediction model uses deep learning-based matrix factorization with the estimated patterns to improve bill prediction performance. The proposed method increases the probability of selecting TOU tariffs that reduce bills. It can be used as an effective tool for recommending tariffs to residential customers, helping them reduce their bills based on the prediction results. An increase in the number of customers selecting TOU tariffs also contributes to improving the stability and reducing the capital investment cost of the power system through peak shaving.

The production systems in process industries are confirmed to be tremendously energy-consuming, and the trust in promoting their energy efficiency has become a concern, with its precondition being to evaluate the real-time energy consumption. A widespread evaluation method is to develop a global model that employs energy audit techniques, whereas they are always carried out with few appreciations of multiple energy consumption patterns, and the utilization of energy consumption auxiliary information. To address the challenge, a two-stage clustering-based-energy consumption evaluation method is proposed for process industries in this study. Specifically, a novel structure of the fuzzy clustering method is designed with a mixture of unsupervised and semi-supervised learning stages that leverages the weighted information to independently address energy consumption patterns. Then energy consumption predictions are estimated for potential energy-optimized control. The key performance indicators of energy consumption are calculated for each pattern, and the final evaluation grade will be achieved through the fuzzy synthetic evaluation method. According to the experiment results, the proposed method delivers better evaluation results against baselines with more accurate clustering; it may provide a new thought for energy consumption evaluation and is confirmed to enable practitioners to acquire the potential benefits in engineering. [ABSTRACT FROM AUTHOR]

This study quantifies China's urban residential CO2 rebound owing to improvements in energy efficiency. We use a dynamic econometric model (error corrected linear approximated almost ideal demand system, ECM-LA-AIDS) to examine the relationship between energy-efficiency promotion, energy consumption behaviour, and the CO2 rebound effect. An improvement in energy efficiency lowers the energy price, resulting in unbalanced short- and long-term energy consumption behaviours. Inconsistent short- and long-term energy-efficiency policies may lead to greater rebound effects. Therefore, this study estimates the residential energy-related CO2 (ECR-CO2) rebound effects considering both short- and long-term consumption patterns to provide targeted policies for controlling residential ECR-CO2. The results indicate that the short- and long-term urban residential ECR-CO2 effects differ across regions and provinces. Additionally, the direct rebound effect contributes more to the total ECR-CO2 rebound effect than the indirect rebound effect. Finally, at the national level, the urban residential ECR-CO2 rebound effects exhibit a U-shaped divergence, indicating that, among the 31 Chinese provinces considered, the ECR-CO2 rebound effect first converges and then diverges, owing to differences in the levels of technological progress. [ABSTRACT FROM AUTHOR]

Time series data analysis and forecasting have recently received considerable attention, supporting new technology development trends for predicting load fluctuations or uncertainty conditions in many domains. In particular, when the load is small, such as a building, the effect of load fluctuation on the total load is relatively large compared to the power system, except for specific factors, and the amount is very difficult to quantify. Recently, accurate power consumption prediction has become an important issue in the Internet of Things (IoT) environment. In this paper, a traditional time series prediction method was applied and a new model and scientific approach were used for power prediction in IoT and big data environments. To this end, to obtain data used in real life, the power consumption of commercial refrigerators was continuously collected at 15 min intervals, and prediction results were obtained by applying time series prediction methods (e.g., RNN, LSTM, and GRU). At this time, the seasonality and periodicity of electricity use were also analyzed. In this paper, we propose a method to improve the performance of the model by classifying power consumption into three classes: weekday, Saturday, and Sunday. Finally, we propose a method for predicting power consumption using a new type of ensemble model combined with three time series methods. Experimental results confirmed the accuracy of RNN (i.e., 96.1%), LSTM (i.e., 96.9%), and GRU (i.e., 96.4%). In addition, it was confirmed that the ensemble model combining the three time series models showed 98.43% accuracy in predicting power consumption. Through these experiments and approaches, scientific achievements for time series data analysis through real data were accomplished, which provided an opportunity to once again identify the need for continuous real-time power consumption monitoring. [ABSTRACT FROM AUTHOR]

IntroductionSo far, many studies have been conducted to evaluate the impact of input consumption patterns on energy, economic, and environmental indicators on horticultural and greenhouse crops in Iran. A review of these studies shows that the causes of the current situation in the systems have not been investigated. These studies are mostly reporting the current situation and the interventions and their effect on improving the input consumption pattern in the sustainability of the system have not been considered by researchers. Also, studies showed that the study location and products do not fit well with the volume of production in the horticultural and greenhouse sector of Iran. Therefore, in order to increase the effectiveness and future direction of studies in this field, this review study was conducted. In this article, Iranian horticultural and greenhouse production systems were reviewed and analyzed by reviewing the published articles between 2008 and 2018, using the PRISMA method. The PRISMA method is a well-known method for conducting systematic review studies. The PRISMA method includes the following sections: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions, and implications of key findings. In this article, 16 types of garden products and 6 types of greenhouse products were studied.Material and MethodsIn this study, the methods used to determine the status of energy consumption, economic and environmental patterns for horticultural and greenhouse crops were analyzed. For this purpose, the indicators of total energy consumption (TEI), energy efficiency (EUE), net energy (NE), and energy efficiency (EP) were examined in the section of energy. The issue of sensitivity analysis of energy inputs was also examined and the highest values of t-statistic and MPP were reported for products. In some articles, the data envelopment analysis method was used in systems performance analysis. The indicators used included technical efficiency (TE), pure technical efficiency (PTE), scale efficiency (SE), and energy-saving target ratio (ESTR). The results of them were summarized and reported. In some studies, the method of artificial neural networks and the Adaptive Neuro-Fuzzy Inference System were used. In general, in the present article, the challenges and risks in the methods used in previous studies were considered. The issue of sampling in the analysis of agricultural systems was discussed in detail and a new sampling procedure was proposed. To draw a general picture of energy and environmental indicators of orchard and greenhouse systems in Iran, the results published in the articles were reviewed. Not all researchers use the same equivalents in calculating the indices, and this makes the results of the studies slightly different from each other. The existence of such differences causes some deviations in comparing the results of similar articles in the same products. However, to adjust for these differences, averaging was used in the index report.Results and DiscussionThe study of the share of inputs in the total energy consumption shows that for horticultural products, the share of fertilizer and electricity inputs is very significant. In the case of greenhouse products, fuel input, which is mainly diesel, has the largest share of energy consumption. Walnuts have the lowest energy consumption and strawberries have the highest energy consumption among orchard products. Grapes, apples, and walnuts also have positive net energy, so they have the highest energy efficiency compared to other products. The most important inputs that have the greatest potential for energy savings in most products are diesel fuel and electricity. Among greenhouse crops in cucumber production, diesel fuel has great potential for energy savings that need to be reduced in future research. In the case of strawberry and rose products, electricity input has the greatest potential for energy savings. Knowing the potential of inputs that can be saved can be effective in changing the behavior of producers.ConclusionTo increase the effectiveness of research in this area, such studies should be done dynamically and for at least two or more years. In the first year, the input consumption pattern should be extracted and after performing the consumption pattern modifying interventions, the effect of these actions should be evaluated in the following years. Data envelopment analysis methods and multi-objective genetic algorithm can be well used to develop solutions to improve input consumption patterns. The review of articles showed that the study of the effect of social factors on the behavior of various production systems has been neglected. Since the pattern of energy consumption in the agricultural sector is significantly dependent on the behavior of users and the characteristics of systems and methods of production, it seems necessary to pay attention to this factor to prepare and design any process improvement strategy in the system. In this study, a new procedure including three stages of analysis, redesign, and evaluation was proposed to complete the studies related to the analysis of agricultural systems.

The college building is a large energy consumer with a high density of energy consumption. However, less attention is paid to college buildings, particularly college dormitory buildings. Based on the one-year historical data collected from 20 college dormitory buildings located in Wuhan, China, this study aims to propose a three-stage strategy to identify and analyze the energy consumption patterns and characteristics of college dormitories in detail, including determining energy consumption patterns, analyzing key characteristics based on four indexes, and examining three influencing factors (occupants' gender and floor and orientation location of rooms). The results show that the heavy energy users (around 10% of all occupants) consume around 20% of the total energy and have the narrowest comfort temperature range. However, the light energy users, 42% of total occupants, consume only approximately 27% of total energy. Their different tolerance to coldness is the main reason contributing to different energy consumption. The dormitories of males and location of the top floor and corner tend to consume significantly more energy in hot weather. This study would help campus facilities to understand the energy use behavior of occupants and formulate adequate policies so as to improve the energy management of campuses. [ABSTRACT FROM AUTHOR]

Energy poverty has become a global problem, a phenomenon that has grown alarmingly due to the crisis caused by the COVID19 pandemic, the crisis of high energy prices in 2021, followed by the invasion of Ukraine by Russia in 2022. These recent events, as well as those associated with the energy transition that occurred in the last decades, have highlighted, in addition to the involvement of government institutions in terms of the necessity of policies in the field of energy, the need to research the phenomenon of energy poverty as well as the need for awareness of the phenomenon among local authorities and of the citizens. [ABSTRACT FROM AUTHOR]

This study examines the impact of higher education on primary energy consumption and non-renewable energy usage for the world's five largest greenhouse gas emitters, namely, China, the United States, India, Russia, and Japan. Autoregressive distributed lag (ARDL)-bounds testing approach to cointegration is employed to annual data series taken from the World Bank's World Development Indicators over the period 1971-2015. The empirical findings validate a U-shaped relationship between higher education and non-renewable energy for Japan, whereas higher education level increases it for China in the long run. Higher education level appears to discourage the usage of non-renewable energy in India and Russia in the long run, but not so effective. From a policy perspective, it urges governments of these economies to provide effective environmental education and knowledge to their people so that non-renewable energy usage would be lowered for a better environmental quality. [ABSTRACT FROM AUTHOR]

• Simple classification method of building energy use without typical model. • Energy use type classification method based solely on energy consumption. • Time-series energy data inventory for planning of energy sharing community. • A method to establish a time-series building energy data inventory. This study aims to establish a foundation for estimating energy demand according to building types by proposing a method to establish a time-series energy data inventory solely based on energy consumption in countries where typical models or benchmarking data by building types have not been developed. First, we presented the characteristics of energy consumption patterns that can be defined from the energy consumption itself based on frequency characteristics. Then, we defined a method to normalize and classify types of daily energy consumption patterns using this method and validated it with measured data. Finally, based on the results, a generalized method for establishing building time series energy data inventory was proposed. This method has a simple procedure and its result can be interpreted intuitively. It does not require data to be converted into stationary time-series data, unlike statistical methods, and it can avoid data distortion due to building operational errors. Moreover, it is easy to find the reasons for the results because the processes are easy to understand, unlike artificial intelligence methods. This study proposes a method to establish a time-series building energy data inventory by classifying types based solely on energy consumption. Furthermore, it suggests the possibility of its application in the early stages of planning an energy-sharing community. [ABSTRACT FROM AUTHOR]

The college building is a large energy consumer with a high density of energy consumption. However, less attention is paid to college buildings, particularly college dormitory buildings. Based on the one-year historical data collected from 20 college dormitory buildings located in Wuhan, China, this study aims to propose a three-stage strategy to identify and analyze the energy consumption patterns and characteristics of college dormitories in detail, including determining energy consumption patterns, analyzing key characteristics based on four indexes, and examining three influencing factors (occupants’ gender and floor and orientation location of rooms). The results show that the heavy energy users (around 10% of all occupants) consume around 20% of the total energy and have the narrowest comfort temperature range. However, the light energy users, 42% of total occupants, consume only approximately 27% of total energy. Their different tolerance to coldness is the main reason contributing to different energy consumption. The dormitories of males and location of the top floor and corner tend to consume significantly more energy in hot weather. This study would help campus facilities to understand the energy use behavior of occupants and formulate adequate policies so as to improve the energy management of campuses.

The multifaceted challenges concerning women's health in hills of Uttarakhand is associated with increasing malnutrition in the State, which ultimately affect the nutritional dividend of the nation. Sub-Himalayan range of the northern Indian subcontinent include regions where an overwhelming number of rural women have always been a part of the active work force in agriculture and allied sector. Present study is an attempt to document the nutritional status of farm women emphasizing their food consumption pattern, dietary intakes and diversification, energy and nutrient balance in Uttarakhand State in the year 2019-20. Collected field level data were analyzed with the help of suitable statistical techniques. It was found that 48 % women in foot hills and 40% women in mid hills had low dietary diversity. The findings also suggest that the prevalence of Chronic Energy Deficiency was high among farm women and energy balances in the respondents (from 171.97 kcal to 415.32 kcal) are corresponding to different grades of BMI. Multivariate regression analysis were employed to drive relationships between high dietary diversity (≥5 food groups) and other independent variables as age, BMI, MUAC, CC, skinfold measurements, body density (D), % body fat, fat mass, fat free mass (kg) . It was found that MDD-W is positively correlated with independent variables (R2=0.702, Adjusted R2=0.721, Standard Error of Estimate=0.0312).

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Social aging will significantly affect energy consumption in the residential sector, with a critical influence on the demand side of the energy sector, especially for megacities. Despite previous studies on the total change in energy demand due to aging, limited research has focused on high-frequency effects. Using a machine learning model, this study examines how social aging will affect household hourly consumption patterns, covering both the change in total consumption and the hourly distribution. We first unsupervised cluster household high-frequency consumption patterns in Shanghai from 2016 to 2018 into 12 groups, and then trained a finite mixture model to analyze the correlation between clusters and household features. We further used the well-tuned model based on the out-of-sample result to simulate the consumption patterns under scenarios of social aging and income growth. The simulation results demonstrate that in addition to increasing overall energy consumption, an aging society will also change the hourly consumption pattern, leading to a larger gap between peak and non-peak periods. • We proposed a behavior-based model to study household energy demand in an aging society. • High-frequency data is used to study household energy consumption changes. • Hourly load curves are simulated in 2030 and 2050 under aging scenarios. • A wealthier aging society will lead to a larger gap between peak and non-peak periods. • Demand reallocation in the aging society is also related to extremely hot or cold conditions. [ABSTRACT FROM AUTHOR]

A rational and reliable energy benchmark is useful for understanding and enhancing building performance while most buildings cannot provide sufficient information for a detailed energy assessment. This work presents a systematic methodology of developing dynamic energy benchmarks for individual office building with very limited information. Simultaneously, an energy consumption rating (ECR) system is established to provide vertical energy assessment for individual office building in a short time span, i.e. hourly. Based on the data produced by DOE prototype large office building model performed in the EnergyPlus environment, this study is conducted in three steps: (1) Step 1: Data preparation; (2) Step 2: Development of the dynamic energy benchmarks; and (3) Step 3: Evaluation of the dynamic energy benchmarks and ECR system. Based on the decision tree analysis, the system energy consumption is classified into eight patterns by few commonly accessible weather and time variables, i.e. outdoor dry-bulb temperature, relative humidity, day type and time type. Then, four energy benchmarks are developed according to four energy consumption patterns on weekdays. To verify the effectiveness of the proposed dynamic energy benchmarks, it is used to evaluate the building energy performance on September, October and November, respectively. Besides, comparative analysis is conducted between the energy baseline (i.e. the same benchmark is used for all energy consumption patterns) and proposed dynamic energy benchmarks. Accordingly, the hourly ECRs were calculated using energy baseline and proposed dynamic energy benchmarks, respectively. Results showed that the energy baseline can be improved by using the proposed dynamic energy benchmarks. And the proposed method is capable of evaluating the energy performance of information poor office buildings. [ABSTRACT FROM AUTHOR]

This study deals with, the estimation of energy consumption, energy flow and energy efficiency in wheat production of Pakistan. A cross sectional data of 200 wheat growers were collected followed by multistage sampling. Results depicted the overall energy consumption of 29787.0 MJ ha-1 in wheat production with an average yield of 3643.3 kg ha-1. Energy efficiency, specific energy, energy productivity and net energy gain were 1.59, 8.18 MJ kg-1, 0.12 kg MJ-1 and 17575.23 MJ ha-1 respectively. Furthermore, Data envelopment analysis approach has been employed for technical efficiency assessment. Fertilizer, diesel fuel and electricity, were the main energy inputs to wheat production, contributing 83% of total input energy. Therefore, for DEA input database, we considered these three inputs (fertilizer, diesel fuel and electricity) as input and yield (kg ha-1) as output. Findings revealed that the average technical efficiency score was 73 %, with a plausible potential of energy saving from 1726.06 to 17013.99 MJ ha-1. [ABSTRACT FROM AUTHOR]

Since the Kyoto Protocol agreement, clean development mechanism (CDM) for techno-economic viable project implementations has garnered large emphasis in terms of certified emission reduction, particularly among developing nations. This paper attempts to assess the economic impact of CDM implementation toward sustainable development and the CO2emission particularly in rural domestic energy sector. A detailed survey was undertaken in the state of Kerala, located in southern part of India, to map the rural domestic energy consumption pattern. Multiple-regression analysis of the data collected reveals the interrelationships of the various parameters that influence domestic energy consumption. The interrelationships between the different parameters were modeled as a linear programming model that optimizes the contribution of individual energy resources on end applications. The results were used to estimate the feasible extent of CO2emission reduction through use of various energy resources, vis-à-vis their economic viability through cost effectiveness. [ABSTRACT FROM AUTHOR]

The heterogeneity in energy consumption between households has long been neglected, leading to the low effectiveness of one-size-fits-all demand-side policies. Here, we investigate energy consumption patterns and home appliance purchasing preferences of different urban households. In this study, 1921 urban households in China were empirically classified into four groups based on their socio-demographic characteristics. It is found that low- and middle-income small-size households have a latecomer energy consumption pattern and a rational home appliance purchasing preference, low-income large-size households correspond to an old-fashion pattern and an economic preference, middle- and high-income large-size households show a moderate pattern and a neutral preference, and high-income small-size households perform a modern pattern and a pro-environmental preference. The inter-group comparison indicates that income increase will promote energy consumption patterns to be more low-carbon and modern. Meanwhile, the demographic transition to small-size family may lead to a carbon-intensive energy lifestyle for middle-income households, while high-income households can have a more saving and green energy consumption pattern. In addition, the scenario analysis indicates that one-size-fits-all policies failed to account for everyone. The findings presented here can inform authorities of the necessity and directions of designing common but differentiated household energy-saving policies. • 1921 urban households are empirically classified into four household groups. • Four energy consumption patterns are identified corresponding to groups 1–4. • There are differences in home appliance purchasing preferences among groups 1–4. • Different households show different levels of response to the same policies. • Common but differentiated household mitigation policies should be designed. [ABSTRACT FROM AUTHOR]

District heating (DH) is recognized as a sustainable energy infrastructure in cities and buildings. DH substation operation is essential to bridging the gap between plants and thermal clients in achieving the intended DH values and visions. Therefore, in this study, an operation analysis method was developed for building-side DH substations. Specifically, Holistic Operational Signature (HOS) was suggested with multiple signature elements (x-OS) and representations to consider comprehensive operational correlations. A HOS-based analytics is proposed to provide more insights and sound reasoning about the DH substation design, operation, control, and heating efficiency potentials, compared with the existing Energy Signature. In a case study for winter, the proposed HOS method was applied to a target DH substation serving actual residential buildings using the real operational datasets. The six HOSs (HOSs 1–6) were derived with their six elements to identify the representative heating consumption levels and patterns, partial heating load ratios, and operation and control efficiency. Excessive water flow rates were identified based on the partial heating load elements (less than 50% in most cases) of all signatures. Inefficient signatures (HOSs 1–4) that do not follow the conventional control principle according to outdoor air temperatures were captured, especially for high heating demand patterns, which accounted for 72.5% of the total in winter. According to the signatures, the demand-based control can decrease the heating supply temperature setpoint by about 1.4 °C and 0.6 °C, respectively for HOS2 and the winter. It is recommended that the valve operation should be improved to lower the hunting levels (within ±1.5 °C) of heating water temperature control, especially in the afternoon with low heating demands. • Holistic Operational Signatures (HOS) are proposed for district heating (DH) substations. • The proposed operation analytics was applied into a real DH substation with field measurements. • Inefficient signatures were captured in the target buildings, accounting for about 72.5%. • Heating supply temperature setpoints were expected to decrease by about 0.6 °C in the winter. [ABSTRACT FROM AUTHOR]

Highlights: [•] Simulating the CTES system behavior based on Malaysian climate. [•] Almost 65% of power is used for cooling for cooling the office buildings, every day. [•] The baseline shows an acceptable match with real data from the fieldwork. [•] Overall, the energy used for full load storage is much than the conventional system. [•] The load levelling storage strategy has 3.7% lower energy demand. [Copyright &y& Elsevier]

To get a proper energy consumption pattern and an increase in energy productivity, determining a relationship between energy inputs and outputs is necessary. In this study, the equivalent energy of inputs and outputs data used in wheat production in Abyek town of Ghazvin province, Iran was collected from farmers over three years. The energy ratio was obtained as 2.11, 2.08 and 2.03 and energy productivity was obtained as 0.15, 0.14 and 0.14 (kg MJ-1) for 2010, 2009 and 2008, respectively. It was found that the contributions of indirect and non-renewable energies on wheat yield were more than the impacts of direct and renewable energies. To determine the effects of energy inputs on wheat yield, the Cobb-Douglas production function was used. Model 1 was composed of individual energy inputs: labor, machinery, electricity, diesel fuel, water for irrigation, fertilizer, chemicals and seed energies In Model 2 energy inputs divided to direct and indirect energies and in Model 3 they divided to renewable and non-renewable energies. The R² values in all three models were more than 0.98 and showed that the models can estimate well. The sensitivity analysis results for Model I showed that the major marginal physical productivities (MPPs) were water for irrigation, human labor and water for irrigation in 2010, 2009 and 2008, respectively. In Model II, the major MPP belongs to for renewable energy in the same years. [ABSTRACT FROM AUTHOR]

• Energy consumption patterns classification was applied to building energy prediction. • Four energy consumption patterns were classified according to the analysis of decision tree. • Ensemble learning models were established to predict hourly energy consumption based on four patterns. • The value of proposed method was evaluated in different training cases. Accurate building energy consumption prediction plays an important role in building energy management and energy policy. However, traditional prediction methods of building energy consumption fail to consider the running conditions of buildings in different periods, which results in the failure of best forecasting effect. This study presents a prediction strategy of building energy consumption based on ensemble learning and energy consumption patternclassification. Hourly meteorological data from a meteorological station and energy consumption data from an office building in New York City are used for this work. First, decision tree is employed to mining energy consumption patterns and classify energy consumption data into corresponding categories. Then, the ensemble learning method is employed to establish energy consumption prediction models for each pattern. Finally, the prediction accuracy of the proposed method is compared with other three methods, i.e., ensemble learning without energy consumption pattern classification, SVR and ANN. Also, the robustness of various methods is investigated by comparing their prediction performance under different training data amounts. Results show that there are four classified energy consumption patterns of the building and significant differences among them. The ensemble learning model with energy consumption pattern classification achieves the best prediction with 17.7%, 16.1%, 15.4%, 15.8%, 15.6% of CVRMSE under 20%, 40%, 60%, 80% and 100% data availability, respectively. It illustrates that the proposed strategy is reliable and effective. Additionally, this strategy can obtain acceptable performance with less training data, which is helpful to the application of energy consumption prediction. [ABSTRACT FROM AUTHOR]

Crucial to the green and sustainable development of China's industry are both choosing an appropriate path of directed technical change and optimizing and improving energy consumption pattern. In this paper we incorporate fossil energy, non-fossil energy and environmental factors into the traditional production function and calculate the factor output elasticity, the growth rate of environmental total factor productivity, the factor-biased degree of directed technical change and factor substitution elasticity in China's industrial sector based on stochastic frontier analysis of the transcendental logarithmic production function. The results show that the high output elasticity of capital leads to the highest factor-biased degree of directed technical change of capital although it is accompanied by an increase in energy consumption and environmental degradation. At present, the factor-biased degrees of directed technical change in China's industry, from high to low, are capital, the environment, fossil energy, labor and non-fossil energy; these can be seen as the root cause of, among other things, overcapacity, energy shortages and environmental pollution. Optimizing the path of directed technical change of labor, capital and non-fossil energy is an advisable way to solve the above problems. The patterns of energy consumption in industrial production in most provinces are unsustainable and each province should optimize its local energy consumption pattern according to those technical changes biased towards non-fossil energy and the positive factor substitution relationship between two kinds of energy. • We calculate the growth rate of environmental total factor productivity. • We measure the factor-biased degree of directed technical change. • The factor-biased degrees of capital and environment are relatively high. • The patterns of energy consumption in most provinces are unsustainable. [ABSTRACT FROM AUTHOR]

Understanding energy users' consumption patterns benefits both utility companies and consumers as it can support improving energy management and usage strategies. The rapid deployment of smart metering facilities has enabled the analysis of consumption patterns based on high-precision real usage data. This paper investigates data-driven unsupervised learning techniques to partition district heating users into separate clusters such that users in the same cluster possess similar consumption pattern. Taking into account the characteristics of heat usage, three new approaches of extracting pattern features from consumption data are proposed. Clustering algorithms with these features are executed on a real-world district heating consumption dataset. The results can reveal typical daily consumption patterns when the consumption linearly related to ambient temperature is removed. Users with heat usages that are highly imbalanced within a certain period of time or are highly consistent with the utility heat production load can also be grouped together. Our methods can facilitate gaining better knowledge regarding the behaviors of district heating users and hence can potentially be used to formulate new pricing and energy reduction solutions. [ABSTRACT FROM AUTHOR]