Actual Energy Consumption Research Articles

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

Building energy performance tools are widely used to simulate the expected energy consumption of a given building during the operation phase of its life cycle. Deviations between predicted and actual energy consumptions have however been reported as a major limiting factor to the tools adopted in the literature. A significant reason highlighted as greatly influencing the difference in energy performance is related to the occupant behaviour of the building. To enhance the effectiveness of building energy performance tools, this study proposes a method which integrates Building Information Modelling (BIM) with artificial neural network model for limiting the deviation between predicted and actual energy consumption rates. Through training a deep neural network for predicting occupant behaviour that reflects the actual performance of the building under examination, accurate BIM representations are produced which are validated via energy simulations. The proposed method is applied to a realistic case study, which highlights significant improvements when contrasted with a static simulation that does not account for changes in occupant behaviour.

Energy Performance, Indoor Air Quality and Comfort in New Nearly Zero Energy Day-care Centres in Northern Climatic Conditions

The European energy policy pushes the member states to transform building stock into nearly Zero-Energy Buildings (nZEB). This paper is focused on data collected from existing nZEB day-care centres,in order to be able to assess possible differences between predicted and actual energy and indoorenvironmental performance. Building structures, service systems and the indoor climate and energyperformance of five day-care centres were investigated in Estonia, Finland and Norway.Indoor climate condition measurements showed that in general, the thermal environment and indoor airquality corresponded to the highest indoor climate categories I and II (EN 15251). Building heating andventilation systems in studied buildings are working without major problems. Good indoor climate conditions were also reflected in the occupant satisfaction questionnaires. For most of the studied buildings, over 80%of the people marked all indoor environment condition parameters (thermal comfort, indoor air quality,acoustics, odour and illuminance) acceptable. The thermal environment in the cooling season was reportedproblematic because it was lower than the minimum temperature for indoor climate category II.Energy consumption analysis showed that measured real energy use was higher, or even significantlyhigher, than the energy use calculated during the design phase. Potential causes of the higher actualenergy consumption are caused by differences of measured and designed solutions, methodology of theenergy calculations, and the differences in user behaviour.Lessons learnt from previously constructed day-care centres can be utilised in the planning and designof new nZEBs.

Energy Performance, Indoor Air Quality and Comfort in New Nearly Zero Energy Day-care Centres in Northern Climatic Conditions

The European energy policy pushes the member states to transform building stock into nearly Zero-Energy Buildings (nZEB). This paper is focused on data collected from existing nZEB day-care centres,in order to be able to assess possible differences between predicted and actual energy and indoorenvironmental performance. Building structures, service systems and the indoor climate and energyperformance of five day-care centres were investigated in Estonia, Finland and Norway.Indoor climate condition measurements showed that in general, the thermal environment and indoor airquality corresponded to the highest indoor climate categories I and II (EN 15251). Building heating andventilation systems in studied buildings are working without major problems. Good indoor climate conditions were also reflected in the occupant satisfaction questionnaires. For most of the studied buildings, over 80%of the people marked all indoor environment condition parameters (thermal comfort, indoor air quality,acoustics, odour and illuminance) acceptable. The thermal environment in the cooling season was reportedproblematic because it was lower than the minimum temperature for indoor climate category II.Energy consumption analysis showed that measured real energy use was higher, or even significantlyhigher, than the energy use calculated during the design phase. Potential causes of the higher actualenergy consumption are caused by differences of measured and designed solutions, methodology of theenergy calculations, and the differences in user behaviour.Lessons learnt from previously constructed day-care centres can be utilised in the planning and designof new nZEBs.

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.

Energy-Constrained Multi-UAV Coverage Path Planning for an Aerial Imagery Mission Using Column Generation

This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.

Strategies for minimizing building energy performance gaps between the design intend and the reality

The disparity found between the predicted energy consumption in the design stage of buildings and the energy use during operation is known as energy performance gap. According to recent studies, the actual energy consumption can be twice to five times higher than predicted by the model. Building energy performance gap can have significant financial and environmental impact. This study aims to address this issue by interacting with professionals in the industry to identify the factors that contribute to the building energy performance gap, and to explore strategies that can help minimise the gap in major office buildings. Semi-structured interviews with 13 building energy professionals were conducted to gather and analyse the relevant design factors that contribute to the gap. Possible strategies of managing the factors were also identified through the semi-structured interviews. The interview responses were analysed qualitatively using Nvivo software, and as a result, 8 factors of energy performance gap were identified, as follows: (i) Inaccurate design parameters, (ii) Failure to account for uncertainties, (iii) Lack of accountability, (iv) Poor communication, (v) Lack of knowledge and experience, (vi) Inefficient and over-complicated design, (vii) Lack of post-construction testing, and (viii) Lack of feedback. Recommendations were drawn from the strategies identified from the study, and a framework on how to address the energy performance gap in the design process was proposed. The framework has three categories, namely strategies for better regulation and better accountability, strategies for project team for accurate energy models, better communication and lack of expertise, and suggestions for training to set training requirements for upskilling the industry workforce.

Effective bulk energy consumption control and management for power utilities using artificial intelligence techniques under conventional and renewable energy resources

Increasing sustainability demands initiate estimating various design and control opportunities for classifying energy-efficient plan ever more significant. These conditions demand simulation algorithms which are not only fast, but also accurate. Artificial intelligence (AI) enables efficient mimicry of bulk energy consumption control while producing results much faster than data-mining and machine learning models. This study proposes two AI based approaches for utilities bulk energy consumption prediction, control and management. Two different zones actual environmental and energy consumption data are obtained for input feature selection and modeling analysis. Each zone is categorized into five features parameter selection (PS) states. Each PS state is further divided into four different hidden neurons (HD) and hidden layers of the model’s network. The forecasting duration is based on 1-month and 1-year ahead intervals for medium-term (MT) and long-term (LT) respectively. Further the current proposed model’s performance is compared with three existing models. One of the promising findings in this research is that substantial improvement in prediction accuracy applying features extracted by PS-3 and PS-5. Results show that AI models are powerful in solving complex and nonlinear patterns of raw data. This study renders optimal decisions can be projected while utilities energy supply strategy & control, capacity expansion, capital investment research market management, revenue analysis and future load requirement forecasting.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

Do environmental concern and future orientation predict metered household electricity use?

Do individuals' environmental attitudes and future orientation predict actual energy consumption? Little is known about the answer to this fundamental question because previous research has relied on self-reported behaviour, which might be prone to social desirability. Therefore, the present study combines survey data with metered data on actual electricity use. Environmental concern is measured by attitudinal items, future orientation by a short version of the Consideration of Future Consequences (CFC) scale as well as a behaviour-based subjective discount rate. Results did not reveal any direct correlations between discount rates and electricity use but mediation analyses suggested small indirect effects. Environmental concern and CFC, however, was positively and considerably related to electricity use. Furthermore, there was a large gender difference, with women using about 23% less electricity than men. This study provides evidence that households' environmental attitudes and future orientation are correlated with actual energy consumption levels, and thus lends support to corresponding educational programmes.

Post-evaluation of energy consumption of the green retrofit building

Retrofitting of existing buildings plays a critical role to achieve sustainable development. This is considered as one of approaches to achieving sustainability in the built environment. In order to evaluate energy consumption of the existing building which had transformed into green building, this article takes the green office building as an example, which located in the summer hot and cold winter zone in China. Collecting the building energy consumption from 2014 to 2016, analyzing energy saving effects by comparing with the standard for energy consumption of building (GB/T 51161-2016) and other cases.Results showed that energy consumption of the case building decreased from 728 MWh to 633 MWh year by year. According to the correction formula in the standard, the corrected value Eoc=72.12 kWh/ (m2·a) is lower than upper limit (85 kWh/ (m2·a)) but higher than target value (70 kWh/ (m2·a)). Therefore, there is large energy conservation potential in air conditioning system and lighting.By comparing with the energy consumption of green commercial buildings in other countries, the result indicates the energy consumption is one-third of the foreign green commercial buildings.the paper provides a reference for the actual operation energy consumption of retrofitting of existing buildings in this area.

Measurement of effective energy consumption in China's rural household sector and policy implication

To facilitate energy system transition, China needs to further optimize energy structure and improve energy efficiency. Compared with physical energy consumption, effective energy consumption can better reflect the actual household energy consumption level. Based on a review of relevant literatures and official statistics, we first estimated the amounts of biogas, dung and solar energy utilized by rural households, and set up the time series of actual rural household energy consumption. The effective energy consumption was measured by the thermal efficiency of fuels from existing literatures, and main characteristics and trend of energy structure change were revealed. Finally, possible scenarios of energy use in 2030 were analyzed. The results show that per capita physical energy consumption and effective energy consumption reached 546.4 kgce and 142.5 kgce in 2015, respectively. The multiple thermal efficiency of all fuel increased from 16.02% in 1990 to 26.08% in 2015, to which the main contributor was energy structure change rather than physical energy consumption growth. The trends including total energy consumption declining and per capita energy consumption growing will continue until 2030. The shares of clean and efficient non-solid fuels in total energy consumption will further increase, whereas the share of renewable energy will decline.

Simulation Analysis and Scheme Optimization of Energy Consumption in Public Buildings

Under the requirement of energy savings and emission reduction in China, building energy consumption, which occupies a rising proportion of the total energy consumption in society, has become the focus of energy conservation research. Public buildings with a high‐energy consumption level have become the most important part of energy conservation research. It is of great practical significance and social value to study energy conservation in large public buildings. In this paper, a large office building is taken as an example. First, a simulation model is constructed by using the energy consumption simulation analysis method, and the reliability of the model is verified by a comparison with the actual energy consumption. Second, based on the model, the thermal design parameters of six building envelope structures, including the external wall heat transfer coefficient, are analyzed in the order of energy‐saving sensitivity. Based on the results of the sensitivity analysis, suggestions for each factor are presented. Finally, considering the mutual influence of each parameter on the building energy consumption, the orthogonal design method is used to arrange the test, and the optimal scheme combination of the energy‐saving effect is analyzed, which can provide decision support for the energy saving of public building envelopes.

Влияние фактического питания и энерготрат на формирование синдрома соматической белковой недостаточности у тамбовских студенток в возрасте 18-22 лет

Aim of the study: to explore the energy status influence features on the formation of anthropometric syndrome of somatic protein deprivation among female students. Materials and methods. 236 girls aged 18-22 years were examined. We study somatometric features of physical development, components of body weight, actual nutrition and energy consumption. We estimate 25 somatometric characteristics measured with application of the automated medical diagnostic complex “Healthy child” (Tulinovsky instrument-making plant, Russia). Syndrome of somatic protein deprivation diagnosis was made by arm muscle circumference index. We carry out the evaluation of actual nutrition and energy consumption by the frequency method with the subsequent application of the computer program “Analysis of human nutrition” (version 1.2.4) of the Scientific Research Institute of Nutrition. Results. With syndrome of somatic protein deprivation daily energy consumption was significantly reduced by 13 %. The main factor determining the level of daily energy consumption among female students is the actual body weight, as well as the amount of muscle mass, which were significantly less with syndrome of somatic protein deprivation than in the control. The level of basic metabolism with syndrome of somatic protein deprivation was also significantly reduced. At the same time, there were no significant differences in the caloric content of diets in these groups. They were in conformity with the age norms. Discussion. Energy costs, including physical activity, appears to be a regulated factor in the formation of somatic protein deprivation syndrome among female students, and its use should be controlled by somatometric signs of this syndrome.

Conclusion and recommendations

This research provided insights into the comfort and energy-consuming behaviours of home occupants and into grouping these home occupants based on their individual differences. This was achieved by using a human-centered approach to an engineering challenge, by assuming comfort as a holistic experience of the home environment, and by treating the ‘occupant-environment’ interactions as a dynamic system. Such an approach drew methods typically used in design and ethnographic research, by gathering both qualitative and quantitative data from both the occupant and the building. The occupant data was collected quantitatively with the use of a questionnaire (self-reported) and qualitatively with interviews (procedural knowledge) and finally with generative techniques (interpretive knowledge). In such a way, different types of occupant knowledge were elicited and collected. The building data was gathered with checklists, monitoring, and energy readings. With the questionnaire data and a clustering technique -the TwoStep cluster analysis- five distinct types of occupant, or archetypes, were discovered and they were progressively enhanced and substantiated with the interview and generative techniques data. Additionally, data of building characteristics, indoor environmental factors, and actual energy consumption completed the details of the archetypes. The following paragraphs provide the conclusion and recommendations drawn from this research. First each of the key questions are answered followed by the answer to the main research question; in which the final description of the archetypes is presented. This is followed by the strengths and limitations of this work and recommendations for the future process. Then for each archetype, environmental design parameters are presented. This finishes with recommendations for future research and the implications of this work.

Home Occupant Archetypes

Home Occupant Archetypes

Rendezvous Cost-Aware Opportunistic Routing in Heterogeneous Duty-Cycled Wireless Sensor Networks

This paper analyzes the packet transmission cost in asynchronous heterogeneous duty-cycled wireless sensor network systems (WSNs). We discover limitations of conventional opportunistic routing protocols when they are applied to asynchronous heterogeneous duty-cycled WSNs. The key reason is that the conventional opportunistic routing protocols overlook the rendezvous cost in calculating the packet transmission cost. To solve the above issue, we introduce a novel routing metric, expected transmission cost (ETC), which is designed with the rendezvous cost-aware. The proposed metric, ETC, appropriately captures the packet transmitting cost in heterogeneous duty-cycled WSN environments by incorporating the estimation for both expected communication as well as rendezvous cost. We then design an efficient ETC-based opportunistic routing protocol (EoR) which selects the best forwarding candidates with the least packet transmission cost to reduce the actual energy consumption of sensors in packet transmission. We conduct comprehensive testbed experiments and simulations with Telosb motes for the performance evaluation of EoR in comparison with the state-of-the-art routing protocols. Obtained experimental results indicate that EoR achieves significant improvements in terms of packet latency, delivery ratio, and energy efficiency, in comparison with the state-of-the-art protocols.

Design and actual energy consumption of heating educational buildings, identification of differences

Design and actual energy consumption of heating educational buildings, identification of differences

Parameters that influence the probability on lower-than-expected energy savings - a pre- and post renovation energy consumption analysis of 90,000 renovated houses in the Netherlands

Thermal renovations are considered to be an effective measure to reduce residential energy consumption. However, they often result in lower-than-expected energy savings. In this paper, we investigate some parameters that influence the probability on lower-than-expected energy savings. We do this by comparing actual pre- and post-renovation energy consumption of 90,000 houses in the Netherlands. The results of this study confirm that the effect of the parameters differ per renovation measure. For every renovation measure, the energy performance gap post renovation plays a significant role. This implies that the use of actual energy consumption data to determine the potential energy savings could therefore help to reduce the number of renovations resulting in lower-than-expected energy savings. Also, the energy efficiency state of the building pre-renovation plays an important role. One should take into account that renovations of energy inefficient buildings more frequently result in lower-than-expected energy savings than renovations of relatively energy efficient buildings. For the type of house we found that multifamily houses more often result in lower than expected savings when building installations are improved, while single-family houses renovations more frequently result in lower energy savings than expected when the building envelope insulation is improved. These insights can contribute to the decision making process whether or not to take a certain renovation measures, they can also help to manage expectations on housing stock level and individual building level.

Temporal and spatial variation in the predictability of building occupancy

It has been widely acknowledged that occupants are one of the major contributors to account for the performance gap between predicted vs. actual building energy consumption. So far, occupancy has been represented as an average value model (deterministic), Markov chain model, survival model, or data mining model. Recent studies by Ahn et al. and Ahn & Park [1, 2] suggest a concept — the “random walk” occupancy model — that in random-walk driven buildings, occupancy is unpredictable. In this study, the authors investigate whether such predictability of occupancy is influenced by temporal and spatial measurement resolutions. For this purpose, occupancy data were recorded in six rooms in a library building for 16 days. Normalized Cumulative Periodograms and the Bartlett test were used to examine the predictability of occupancy. It was found that the occupancy of a small group (in this study, occupants numbering 18 or fewer) is unpredictable and can be regarded as a random-walk driven space. In contrast, when the number of occupants is equal to or more than a certain number (in this study, 27 people) either in a single large space or aggregated over multiple spaces (up to the whole-building level), occupancy becomes predictable. Additionally, it is shown that the predictability of the time-series occupancy data is not significantly influenced by varying the temporal resolution (sampling interval) between 5 and 60 min, but is dominantly driven by the number of occupants.

Energy efficiency state of non-profit housing stock in the Netherlands

Improving energy efficiency of buildings is widely considered as the one of the most promising, fast and cost-effective ways to mitigate climate change and achieve the 2020 and 2050 goals set for the built environment (European Commission 2011; Aedes 2017). Energy efficiency of the building stock is hard to achieve if we only focus on the design of new dwellings. In this chapter, we will analyse the energy efficiency state of the existing Dutch non-profit housing stock using data from 2015. We examine the energy efficiency state of the stock using data from the SHAERE monitoring system. The data from SHAERE include the age, type, useful floor area, thermal resistance (Rc-value) of the envelope (roof, facades and floor), thermal transmittance (U-value) of the windows, heating and domestic hot water (DHW) systems, ventilation system, the predicted heating energy consumption and energy production systems, if present. We also use actual heating energy consumption data from Statistics Netherlands to calculate the mean actual energy consumption of the stock. The chapter aims at setting the current energy performance state of the Dutch non-profit housing stock. A complete and detailed assessment of the current efficiency state of the non-profit housing stock in the Netherlands is necessary in order to examine the energy renovation pace and energy saving measures realised. The following sections present the results and conclusions drawn from this chapter. Section 2.2 presents the development of energy efficiency policies in the built environment. The SHAERE monitoring system is presented in 2.3. Section 2.4 sums up the methods used. Section 2.5 brings attention to the results of the research study. Section 2.6 presents the conclusions of this chapter.