Energy Consumption Patterns in Urban Buildings: History
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Lazim Abdullah
,
Mohsen Ahmadi
,
,
Behdad Arandian

Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models.

  • energy consumption
  • urban building
  • fuzzy logic

1. Introduction

The residential construction industry represents one of the world’s largest energy consumers, and it is economically and environmentally vital. As energy consumption increases, so do emissions, which are widely recognized as the primary cause of climate change and its consequences. As a result of these concerns, governments and international organizations have been increasing efforts to balance energy production and consumption with environmental concerns [1]. In order to ensure long-term energy security, the energy–demand balance should consider not only how to generate energy, but also how to increase performance. If the systems are under-estimated, they may not be able to meet the comfort needs of the inhabitants [2]. The energy consumption of a building is determined by its thermal condition and the behavior of its occupants. Assessing and quantifying occupant behavior is a more challenging task than evaluating the exterior and thermal condition of a building. Researchers have studied this issue for many years. According to research, the estimation of occupants’ energy consumption can be performed by identifying their patterns of behavior (occupation status, number of occupants, placement of occupants, action taken by occupants) [3]. Accordingly, this study explores a novel method of assessing building energy consumption by identifying and quantifying inhabitant activity in terms of time usage data.
In order to meet the growing global energy demand, advanced forecasting methodologies are required. Economic and non-economic variables are used to estimate energy demand, which can be determined using linear and non-linear analysis tools, arithmetic, and modeling techniques [4]. The non-linearity of these variables and energy consumption has led to the creation of intelligent solutions, such as evolutionary computation, fuzzy models, neural networks, and convolutional neural networks for non-linear analysis and simulation. Modeling energy usage is typically based on the previous usage. Financial, social, and environmental factors influence energy consumption. Currently, data processing is an area of interest for a number of researchers, which has led them to focus on the issue of power generation. Simulation is beneficial and efficient in some areas of policy development. It is, therefore, impossible to achieve energy security without a thorough understanding of historical and current energy use, as well as possible future demands. Developing models and projections of energy consumption is essential for legislators and other groups involved in the development of countries [5]. It is possible that consumption ignorance can lead to power shortages, which can have catastrophic effects on economies and societies. An overestimate of energy consumption can result in excess capacity, leading to financial waste. Consequently, to avoid making incorrect decisions, it is preferable to use models that provide more precise predictions of energy consumption. Additionally, it is preferable to employ a predictive model to handle non-linear energy usage data. Based on prior studies, multiple regression is the most common method of forecasting energy consumption. Nonetheless, for potential users, such as energy scientists, the meta-heuristic method is more appealing and meaningful since it enables more reliable energy utilization, regardless of the time savings [6]. Additionally, this technique offers fast computation, cheap efficiency, and ease of use for those with less technical knowledge. Due to this, using artificial neural networks (ANNs) for analysis and simulation is an objective that has been pursued throughout the past decade [7]. As a result, ANNs offer a number of advantages, including faster processing times, shorter development times, and superior estimation capabilities. Artificial intelligence is particularly effective for preempting unclear situations. There are no mathematical formulas or background knowledge for the inputs and outcomes. By using electricity, financial, capital, and geographic data, this research aims to estimate and anticipate power consumption in Iran. Due to the complexity of architectural power generation, ANNs are an effective tool for non-linear evaluation.
ANNs are composed of layers of discrete units called “neurons”. In a typical network, each neuron in one layer is connected to all neurons in subsequent layers [8][9]. The ‘weights’ are the connections between neurons. Using a neural network, these weights are assigned appropriate quantitative values. This can be accomplished through the use of training data, which involves feeding the network a collection of real data. As a result of these possible combinations, the network learns and adjusts its weights accordingly. ANNs are capable of learning from past experiences. A conventional, feedforward neural network is employed in this investigation. In a feedforward system, data travel only in one direction, forward, from the layers of neurons to the output neurons, passing through any hidden neurons (if any).
The collection of resident activity data is essential for assessing the effects of usage on building energy performance. Simulation-based research was conducted in order to achieve this goal [10][11][12]. Carlucci et al. (2016) [13] examined the resource efficiency of a residential structure in Shanghai using stochastically generated occupancy patterns. Based on the analysis, the energy performance unpredictability was as high as 10% due to the unpredictability of occupancy patterns. According to the research, this inconsistency was also more noticeable in high-performance buildings than in poorly insulated structures. Motuziene and Vilutiene (2013) [14] analyzed the residential sector in Lithuania and found that the number of inhabitants, the age of the inhabitants, and their behavior all affected energy use for heating, illumination, and air flow. As a result of evaluating various occupation characteristics, the energy usage of the building varied from 13% to 30%. In terms of forecasting time series, numerical and analytical approaches proved very useful. Nevertheless, they are not without certain disadvantages, such as the fact that the consequence form of the study variables may not be well described if the methodologies are not properly understood. In addition, outdated data can lead to biased estimates of pattern parameters. Additionally, although most time series patterns are linear, they cannot describe non-linear processes.

2. Energy Consumption Patterns in Urban Buildings

Reduced energy consumption has been proven to be one of the most cost-effective ways to improve energy conservation. Often, efficiency improvements can be achieved by using less energy to accomplish the same tasks or achieve the same objectives. Matar (2016) [15] discussed the impact of improving home energy efficiency on power consumption patterns in Saudi Arabia. The improvement in the energy efficiency of air conditioners from 7 to 11 would result in a reduction of 225,000 tons of oil used in power generation each day. In contrast, increasing the insulation level from 27 to 64 percent would save 158,000 barrels of oil per day. The study of Al-Tamimi (2017) [16] examined the policy initiatives to improve energy efficiency in Saudi Arabian buildings and concluded that steps must be taken to embrace energy-efficient technology in the construction industry. An extensive investigation of the effects of various energy-conservation techniques on residential energy consumption in the Kingdom of Saudi Arabia was performed, including changes to exterior and interior walls, window designs, shading, exterior surface color, flow velocity, and thermal crossings [17]. Jiang et al. (2021) [18] examined a broadband cancellation technique for adaptive co-site interference cancellation systems. As a consequence, the simulations and tests support the theoretical analysis validity and efficacy. Li et al. (2021) [19] describe the impact of natural and social environmental elements on building energy usage. Findings indicate that multidisciplinary interactive research that utilizes dual viewpoints of natural and social contexts is likely to generate new ideas. Researchers have recently focused on adoptive neuro-fuzzy inference systems (ANFIS), which combine fuzzy if–then rules into a neural-network-like structure [20]. The first-order Takagi–Sugeno system, which was extensively used in increasing energy prediction research, forms the structure of the ANFIS utilized here. Dong et al. (2021) [21] developed a classification-based, ensemble-learning approach for energy-use prediction. In their research, hourly weather data from a weather station were used, along with energy usage information from a New York office building. To begin, a decision tree was used to mine energy usage trends and categorize data into appropriate groups. The ensemble-learning approach was then applied to each pattern to create energy consumption projections. It was demonstrated that the recommended method was both reliable and effective. Furthermore, this method was able to obtain adequate results with a minimal dataset, which is beneficial for applications that forecast energy consumption. To estimate building energy use, Somu et al (2021) [22] developed a k-CNN-LSTM, which uses electricity gathered at specified intervals. The summary of the research shows in Table 1.
Table 1. A summary of the research in the field of building energy pattern prediction.
Author Objective Method Results
Popoola and Chipango (2021) [23] The residential building energy pattern Improved peak-load management control technique It was found that maximum use and energy consumption decreased significantly, ranging from 3% to 20%, for the time-of-use intervals, and at least 14.05% for the energy efficiency.
Ali et al. (2021) [24] The institutional building energy pattern Statistical analysis Inspection results confirmed the structure’s electricity bills, which ranged from 160 MWh to 250 MWh and RM 80 k to RM 120 k per month, on average.
Somu et al. (2021) [22] The four-storeyed building energy pattern k-convolutional neural networks and long-short-term memory It was noted that the effective electricity consumption estimate produced by kCNN-LSTM is an excellent deep training algorithm for power consumption prediction issues due to its capacity to understand the spatio-temporal relationships in the energy data.
Dong et al. (2021) [21] Office building energy pattern Ensemble learning based on SVR and ANN It illustrated the viability and effectiveness of the suggested plan. Additionally, this method provided satisfactory results with minimal training data, which is beneficial for energy usage projection applications.
Mokhtari and Jahangir (2021) [25] University building energy pattern NSGA-II algorithm According to the findings, an ideal demographic makeup can lower the number of sick persons by up to 56% while also reducing energy usage by 32%. Additionally, virtual training was an effective way for colleges to reduce the number of illnesses and energy usage.
Barik et al. (2021) [26] Electric regulation in hybrid smart grid Quasi-oppositional chaotic selfish-herd optimization The research examined the voltage, frequency, and tie-line power synchronization of the prototype solution under five severe scenarios of source and load fluctuations without adjusting the regulators. In addition, ten different potential configurations of modules in different microgrids were examined in order to determine the optimal combination. In summary, the results of this study indicate that implementing the suggested approach increases the effectiveness of distributed microgrids.
Brandi et al. (2020) [27] Office building energy pattern Deep reinforcement learning If the set of variables is appropriately specified, it should be possible to achieve a heating energy savings between 5 and 12% with improved interior temperature management and static and dynamic deployment. Lastly, the study showed that if input variables are not selected correctly, a dynamic deployment is necessary to achieve satisfactory results.
Li et al. (2020) [28] University building energy pattern Decision tree, Adaboost, and RandomForest In the case of intermediate usage hours, school scales must be considered. The AC set degree is a crucial control parameter for long-term AC operation. This study contributed to more realistic energy demand simulations and more efficient energy management in educational facilities.
Fahim et al. (2020) [29] Smart building energy pattern One-class support vector machine, Markov transition function An extensive public information database was used to test the proposed model. The results of the acquired studies were comparable and demonstrated the effectiveness of the TSI model in actual situations.
Ashouri et al. (2020) [30] Role of occupants in building energy consumption Statistical analysis As compared to previous state-of-the-art systems, the present system improved accuracy, adaptability, and realistic findings.
Irtija et al. (2020) [31] Energy demand managment in smart grid Standard convex optimization methods On the energy market, it was determined whether or not the prosumers are aware of their kinds, and the ideal contract was negotiated between parties who have competing interests. A power contract that meets ideal conditions includes both the quantity of power purchased by prosumers and the incentives provided by the electricity market. It was demonstrated that a contract-theoretic approach has both advantages and disadvantages.
Wen et al. (2020) [32] Forecasting of buildings’ energy demands in smart grid ANN, LSTM, RNN It appeared that the new model predicted aggregated and disaggregated energy demand for residential structures more precisely than existing approaches. In addition, the proposed deep-learning model was an excellent way to fill in any missing information based on historical data.
Von Korff (2019) [33] Energy-load analysis for zeo-net energy Machine-learning methods By measuring the net electrical consumption and output for each residence over the course of a year, the researchers provided a variety of typical energy demand profiles. The load profiles presented a number of ways in which solar power or energy storage could be beneficial to customers or grid operators. Additionally, several inefficiencies within the existing system were discussed, along with recommended solutions. As a result of utilizing machine learning to analyze the preliminary data collected from the first advanced energy communities, electric grid managers were better equipped to prepare for a large-scale deployment of solar power and energy storage systems.
Li et al. (2018) [34] Residential building energy pattern Deep belief network and generalized radial basis function neural network It was shown how useful it is to include electricity behaviors. This method may be applied to other similar periodicity-based prediction problems, such as traffic flow prediction and power-usage prediction.
The k-CNN-LSTM utilizes k-means clustering to determine the energy usage template and convolutional neural networks (CNN) to retrieve advanced structures with non-linear connections that affect energy consumption. A long-short-term memory (LSTM) artificial neural network can be used to represent temporal features in time series analysis. It should be noted that the precise energy usage prediction produced by k-CNN-LSTM is an excellent deep-learning model for issues of energy usage forecasting due to its ability to understand spatial and temporal relationships within the datasets.
Li et al. (2021) [35] proposed a unique transfer learning approach for detecting cross-scene pavement discomfort. Zhang et al. (2021) [36] proposed friction-based isolation solutions for masonry buildings. Findings show that a reduction of 45 to 56 percent was observed in absolute growth, depending on the kind of ground motion. This reduction was mainly caused by the isolated building’s top roof level. Wang et al. (2021) [37] suggested a technique for electrical substation fault diagnostics based on a rough set-based bio-inspired fault model. The suggested solution outperforms existing options in experiments conducted on genuine 110 kV and 750 kV substations. Popoola et al. (2021) [23] presented a method for controlling maximum demand based on the ranking of end-use appliances and event identification. The appliance that was selected by the customer was one of the most valuable components of the approach since it allowed inhabitants to adjust their load power to meet their demands at any time, regardless of whether generation capacity management was active or not. Findings indicated that peak usage and energy demand can be reduced by 3% to 20%, with energy savings of 14.05% for the time-of-use periods and energy efficiency. These provide a new, cost-saving relationship between energy consumption and load consumers, which offers a fresh perspective on load forecasting. Wang et al. (2021) [38] proposed a weighted corrective fuzzy reasoning spiking neural P system for fault identification in variable-topology power systems. In Mokhtari and Jahangir’s study (2021) [25], the objective was to determine the best occupant distribution that resulted in the lowest number of sick people and the least energy consumption. A university building in Tehran was selected as a case study due to its versatility in implementing various occupant distribution patterns. Using the objective functions of electricity consumption and COVID-19-contaminated persons, the NSGA-II method was used to solve this multi-objective optimization problem. In the research, it was found that an ideal population distribution could reduce the number of sick people by up to 56% while simultaneously reducing energy consumption by 32%. Additionally, virtual learning helped colleges reduce the number of illnesses and energy consumption. The above table summarizes research in the field of building energy pattern prediction.

This entry is adapted from the peer-reviewed paper 10.3390/math10081270

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