Life Cycle Assessment Techniques Research Articles

Application of the Life Cycle Assessment (LCA) Method in Assessing the Environmental Impact of New Materials Derived from Waste Polymers in Terms of Sustainability

Sustainable socioeconomic development should provide humans with a suitable environment for safe living. It can be debated whether the term “environment” should be used due to the significant anthropogenic transformation of the environment. Therefore, an essential part of solving environmental problems is innovation. Climate, resource conservation, and environmental protection are recognized worldwide as common challenges. Thus, it is necessary to implement solutions that simultaneously protect the environment and the climate with sustainable and rational use of resources. Related to this issue is the principle of a loop/closed-loop economy. Among other things, it refers to using waste to prepare materials that can be used for other purposes. The use of tools such as LCA (life cycle analysis) contributes to supporting environmental protection. With the LCA method, it is possible to analyze environmental risks and compare new technological alternatives. LCA is a methodology that has been used around the world with great success, especially for studying individual stages of the entire product life cycle. The results of studies that have been conducted in various research centers confirm the possibility of also using the LCA technique for the environmental assessment of new technologies or existing modernized technological processes. The purpose of this study was to assess the feasibility of using the LCA method to determine the environmental impact that the potential production and use of new materials will have.

Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques

Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques

Biodiversity impact assessment for finance

AbstractBiodiversity loss, driven by human activities, significantly affects the environment, human societies, and economies. Using the extended multi‐regional input–output (EEMRIO) and life cycle assessment (LCA) techniques, we offer insights into how these methodologies can be used to inform financial decisions related to biodiversity focusing on two key aspects: biodiversity impacts and ecosystem service dependencies. Our method combines spatially explicit characterization factors from LC‐IMPACT with the Global Resource Input‐Output Assesment (GLORIA) database to estimate biodiversity impacts. As a case study we assess the biodiversity impact of the MSCI All Country World Index (MSCI ACWI) which consist of about 3000 large‐ and mid‐sized companies, from 23 developed and 24 emerging countries. The results demonstrate that most of the biodiversity impact is caused in the Americas, followed by Asia, despite its low representation in the index's country weight (6%). Europe shows the least impact. These results emphasize the need to account for global supply chain linkages as products sold in one country might have significant biodiversity impacts elsewhere due to sourcing of production inputs. Second, our results identify the main determinants of the index's impact: land use, followed by water stress and climate change. Although most of the impact is localized in few sectors, the distinct characteristics of these sectors require industry‐specific mitigation approaches. Finally, double materiality results show both, the influence companies have on biodiversity and the reciprocal effects. Companies neglecting these impacts risk financial setbacks, making it a crucial concern for investors.

Evaluation of environmental impacts of mobile phones in India using life cycle assessment technique

Evaluation of environmental impacts of mobile phones in India using life cycle assessment technique

Analysis of the life cycle concrete with the addition of polypropylene fibers

In accordance with the principles of sustainable construction, the results of Life Cycle Assessment (LCA) technique are useful inputs to the decision-making process when designing a building. This article presents such an analysis of a finished building product, which is a modified concrete mix. The calculations took into account the phases of the A1–A4 cycle, i.e. from the extraction of raw materials to the transport of the finished material to the construction site. Test results for concrete mixes and 28-day solid concrete are presented in tabular form. Based on all the test results obtained, it was found that the addition of waste polypropylene fibres has a positive effect on the key properties for the floor concrete. It has been found that proper processing of banding tapes or other polypropylene waste into macro-fibres can be a good example of proper waste management and can contribute to a significant reduction in residual waste. This additive is emission-free and sourced from recycling, making it an excellent alternative to commonly used dispersed reinforcement.

The pitfall in designing future electrical power systems without considering energy return on investment in planning

Measuring the limitations of transitioning to 100% renewable energy systems partly equates with the capability of quantifying associated net-energy production. To foresee future possibilities, an advanced systemwide energy-return-on-investment (EROI) model was constructed. Driven by the thorough consideration of the existing weaknesses of EROI, the model integrates the concept of energy statistics, life cycle assessment techniques and energy system modelling in order to overcome the underlying primary energy quality issues, boundary condition problems and energy system design related uncertainties that compromises comparability of EROI during the energy transition. This model was applied to the Israeli power system that presents a unique opportunity to examine the vulnerability of depending on limited resources and the connection of EROI to system design. This study demonstrates the dependency of EROI on the energy transition path, system design requirements expressed via interactive linkages of curtailment, variable renewable energy penetration and storage system design, and resource diversity. Achieving a very high variable renewable energy penetration by 2050 with solar photovoltaics carries a risk of falling below an EROI value of 10, such vulnerability can be largely minimised by well-conducted manoeuvring of the system design and resource diversification. Despite the large uncertainty of this study, the need for a multicriteria designing technique is out of the question.

Investigating social and environmental hotspots throughout the lifecycle of product-service systems in the early design stages

Product-Service Systems (PSS) have been recognized as potential game-changers for advancing sustainability. However, the sustainability of a PSS over traditional products and services is not automatic; it requires careful, intentional design. Despite a decade's progress in relevant literature, early-stage design assessment of a PSS – particularly from a life cycle standpoint and considering the social sustainability dimension – remains underexplored. This study introduces a framework for early-stage identification and analysis of potential environmental and social impacts within a PSS's life cycle to aid in decision-making and guide towards sustainable solutions. The framework's hotspot analysis, which combines life cycle assessment techniques, aims to pinpoint life cycle stages most susceptible to significant environmental and social impacts. Drawing on existing literature and expert evaluations, this approach offers designers preliminary insights into potential impacts, establishing a groundwork for comprehensive life cycle analysis once more detailed information on products, services, and organizational structures becomes available. The hotspot analysis not only addresses the lifecycle view of a PSS but also places emphasis on the oft-neglected social dimension of sustainability, marking an area ripe for further study. By supporting decision-making in the early design phase and encouraging scrutiny of various PSS concepts, this approach promotes the cultivation of sustainably-designed solutions.

A Comprehensive Review of Life Cycle Cost Assessment of Recycled Materials in Asphalt Pavements Rehabilitation

This paper provides a comprehensive review of the use of life cycle cost assessment (LCCA) and life cycle assessment (LCA) methods for evaluating the sustainability and costs of using recycled materials in asphalt pavement rehabilitation projects. The review begins with an introduction to pavement rehabilitation strategies and the importance of choosing techniques based on thorough engineering and economic analyses. It then explores the different types of recycled materials that can be utilized, including reclaimed asphalt pavement, recycled concrete aggregate, and recycled asphalt shingles, discussing the key characteristics and properties of these materials based on previous laboratory studies. The review also examines the various rehabilitation methods that employ recycled content, such as cold in-place recycling, hot in-place recycling, and full-depth reclamation, providing a detailed breakdown of the construction, maintenance, and rehabilitation costs considered in LCCA and analyzing the environmental benefits of recycled material usage through a review of LCA techniques and criteria like carbon footprint reduction, impacts on air and water quality, and considerations of technological factors. Software tools for conducting LCA are compared and challenges to advancing the adoption of recycled materials are reviewed along with directions for future research efforts. The unique contribution of this work is its holistic assessment of LCCA and LCA methodologies to inform the sustainable and cost-effective deployment of recycled materials in asphalt pavement rehabilitation, a topic of growing importance for transportation infrastructure management. In summary, this current work provides a valuable review of how LCCA and LCA methodologies can assess the sustainability and costs of employing recycled content in asphalt pavement rehabilitation projects.

Biodegradable mulch film enhances the environmental sustainability compared with traditional polyethylene film from multidimensional perspectives

Considering the minimum environmental concerns associated with biodegradable mulch (BDM) films, these are widely seen as a promising alternative to traditional polyethylene (PE) films. However, lack of an in-depth evaluation of BDM plastic films has hindered its largescale applications. To fill this knowledge gap, this study was planned to comprehensively evaluate the environmental impacts of BDM films as an alternative to the traditional PE film utilizing an advanced life cycle assessment (LCA) technique coupled with multiple field experiments, including three solanaceous crops i.e. eggplant, pepper and cherry tomato. These crops were grown at three different experimental sites comprising Chengmai, Qionghai, and Ledong; all located in Hainan Province of China. The results revealed that an in-situ biodegradation of BDM during the waste disposal stage greatly reduced the environmental pollution and cumulative energy demand (CED). But such a positive effect was largely nullified by the stimulated environmental cost during its manufacturing stage. And thus, the average environmental impact and CED from cradle to grave were reduced by only 2.6% and 9%, respectively, under BDM film when compared with PE. The seven sites/years-based field trials of three solanaceous crops revealed that the average crop yield under both plastic mulching cultivations was increased by 52%, while the integrated environmental impact per ton yield was reduced by 30%, compared with conventional bare planting (CP). Overall, it can be inferred that BDM film is a sustainable alternative to traditional PE mulch film based on its reduced environmental impacts.

Water Footprint: A Review Based on Concepts, Methods, and Implications

Water footprint assessment has become a useful method for assessing how much water is used by products and activities related to humans, offering insights into how water resources might be managed sustainably. While agricultural production accounts for the majority of water consumption worldwide, the industrial and residential sectors also use large amounts of water and cause pollution. This review article provides an examination of the approaches and uses of water footprint assessment in a range of industries and geographical areas. The notion of water footprint and its elements green, blue, and grey water footprints as well as the corresponding computation techniques are introduced at the outset of the work. The most recent developments in spatially explicit modelling and life cycle assessment techniques, it examines the development of water footprint assessment procedures. The paper explores the many uses of water footprint assessment, from consumer items and urban water management to industrial operations and agricultural output. It provides case studies and real-world examples that show how water footprint analysis may be applied practically to address issues with pollution, water shortages, and sustainability in various settings. The study also looks at the difficulties and constraints that come with assessing the water footprint, such as methodological uncertainty, data availability, and temporal and geographical variability. It talks about current studies as well as potential paths forward for improving the reliability and usability of water footprint measurement instruments. As a comprehensive and integrated method for measuring and controlling the effects of human-induced water usage on regional and global water resources, this study concludes by highlighting the importance of water footprint assessment. In order to promote sustainable water management practices, it highlights the necessity of multidisciplinary collaboration and stakeholder involvement. Keyword: Water Footprint, Sustainability, Life Cycle Assessment, Spatial Analysis.

Sustainable waste management and waste-to-energy in the context of a circular economy through various waste management technologies.

Inappropriate waste management is a considerable ecological risk, leading to detrimental effects on the soil, air, and water quality. It is imperative to address these concerns promptly to minimize the repercussions of solid waste on public health and the ecosystem. It is evident that the level of economic growth directly impacts waste generation. This study intends to use the life cycle assessment (LCA) technique to evaluate the environmental impacts of four alternative municipal solid waste (MSW) management scenarios in Peshawar City, Pakistan. The goal is to discover an option that is both sustainable and minimizes environmental damage. The study examined the system boundaries encompassing the collection and transportation of MSW, along with its processing and final disposal, employing composting, anaerobic digestion (AD), material recovery facilities (MRF), and landfill methods. Comprehensive field studies and an in-depth literature review provided the data regarding Peshawar's existing MSW management system and the proposed scenarios, all of which was inventoried in the OpenLCA 1.10.3 database. Following data collection, the CML-IA technique was employed to analyze the data, measuring the environmental footprint in terms of climate change potential, human toxicity, acidification potential, photochemical oxidation, and eutrophication. A sensitivity analysis was also performed to identify the influence of varying recycling rates on the environmental strain correlated with the proposed scenarios. The analysis results indicated that scenario S2, which combined composting, landfilling, and MRF, exhibited the least environmental impact compared to the other considered scenarios. Furthermore, the sensitivity analysis reflected an inverse correlation between alterations in the recycling rate and the total environmental impact. To counter the environmental problems arising from waste generation, it is essential to incorporate principles of the circular economy into the MSW management approach.

Synergism of Life Cycle Assessment and Sustainable Development Goals Techniques to Evaluate Downflow Hanging Sponge System Treating Low-Carbon Wastewater

While recent researchers have focused on optimizing the operational conditions of low-carbon wastewater treatment processes, they have not sufficiently evaluated the sustainability of such systems. This study is the first to assess the performance of a low-carbon wastewater treatment facility using an integration of techno-economic and life cycle assessment (LCA) criteria accompanied by several sustainability indicators. A downflow hanging sponge (DHS) reactor was operated at a hydraulic retention time of 3.4 h, an organic loading rate of 3.8 kg COD/m3 sponge/d, and 24–35 °C (scenario_1). Another two DHSs were operated in parallel, i.e., a 50% influent bypass (scenario_2) and 260 mg/L charcoal addition (scenario_3), providing carbonaceous matter to maintain the nitrification/denitrification pathway. Employing the DHS’s scenario_3 could fulfill most of the SDGs regarding the environmental (e.g., COD and nitrogen removals) and socio-economic (e.g., reliability, labor, and health and safety) targets. The LCA tool also confirmed the superior environmental benefits of scenario_3, concerning effluent quality, GHG emissions, and sludge generation. The synergistic interaction of LCA and SDGs approaches ranked the proposed DHS modifications as scenario_3 > scenario_1 > scenario_2. Hence, the current study provided an innovative strategy that could be employed to assess the sustainability of wastewater treatment systems worldwide.

Sustainability analysis of irrigated and rainfed wheat production systems under varying levels of nitrogen fertilizer through coupling of emergy accounting and life cycle assessment

Most of the crop production systems have strived for improving crop productivity by increasing various resource inputs; especially water and synthetic nitrogen (N) fertilizer, without considering their environmental consequences. Yet, only few studies have evaluated the sustainability of cropping systems simultaneously using life cycle assessment (LCA) and emergy accounting (EMA) techniques. The former highlights pollutant emissions and environmental impacts, while the latter emphasizes resource consumption and system efficiency. In this three-year field study, the environmental impacts and sustainability of farming systems were estimated under rainfed and irrigated patterns at four N application rates in the Northwest China. Our result indicated that the irrigated pattern enhanced grain yield by 9%, while simultaneously increasing 8% of environmental impact and 12% of emergy input based on per hectare of wheat production. Almost similar results of environmental impact per kg basis of grain production and emergy sustainability index revealed that irrigation practice did not decrease the overall sustainability of a cropping system. Conversely, the higher N levels of 240 and 360 kg N ha−1 did not provide a proportionate yield return and were accompanied by increased environmental impacts of 20% and 69%, respectively on per kg grain production basis, when compared to an N dose of 120 kg N ha−1. Meanwhile, the gross unit emergy value revealed that the production efficiency under 120 kg N ha−1 was enhanced, in comparison with other N levels. Therefore, a moderate N dose of 120 kg N ha−1 under both irrigated and rainfed patterns is highly recommended for adoption due to its overall advantage in terms of yield returns, environmental impact and production efficiency.

Eco-efficiency of power supply systems for offshore platforms

This study compared from an Eco-efficiency point of view three electricity supply alternatives for oil and gas extraction, preliminary treatment, and distribution activities that occur on floating production, storage, and offloading platforms (FPSO) operated off the coast of Brazil. The performance of autonomous systems (AS), in which independent open-cycle gas turbines generate electricity, was confronted with those achieved, respectively, by a Power Hub (PH) system and by an arrangement that associates the Power Hub system with an offshore wind farm (PHWE). The environmental dimension of the Eco-efficiency indicator was specified based on applying the Life Cycle Assessment technique for the impact categories of Global Warming Potential, Primary Energy Demand, and Fine Particulate Matter Formation. The economic dimension followed a similar approach based on Life Cycle costing (LCC) when considering the same units' construction, operation, maintenance, and dismantling costs. Given the characteristics of Brazilian oil fields, a comparison was made for three different stages of the operation cycle: C1: maximum extraction of oil and gas; C2: equity of volumes extracted from fuels and water + CO2; and C3: minimal extraction, but economically viable, of fuels. The results indicated that the PH and PHWE systems accumulate better environmental performance than the AS system, particularly regarding Global Warming Potential (35 % and 55 %, respectively) and Fine Particle Matter Formation (47 % and 59 % reduction). However, more than these gains are needed to compensate for the same systems' construction, operation, and maintenance investments, which were increased by up to 76 % in the case of PHWE. The study also concluded that the PH technology and its variation involving wind electrification are promising but that research efforts should be made to reduce costs. These results provide elements that can both subsidize designers from O&G companies in developing new extraction technologies and support decision processes conducted by other stakeholders and policy formulators linked to this segment.

Quantifying the carbon footprint of energy storage applications with an energy system simulation framework — Energy System Network

Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.

Sustainable Management of Red Mud: Life Cycle Assessment and Treatment Techniques

Red mud is a strongly alkaline leaching solid hazardous waste material generated in the Bayer process of alumina production. India ranks 4th place in alumina production. According to the Ministry of Mines (Oct 2022), India produces 9 million tonnes of red mud annually. In nature, red mud is highly alkaline. The high alkalinity and caustic content of red mud will contaminate the groundwater and fertile soil, and also there is a limitation on the utilization of red mud. Due to its low utilization ratio, large amounts of red mud are stacked in disposal areas, which may cause water pollution, soil salinization and heavy metal pollution. Red mud contains toxic metals such as arsenic, chromium, lead, nickel, zinc and radioactive elements such as thorium, potassium, and uranium, which cause severe effects on the environment and also on our health. The objective is to examine the environmental impact, utilization methods and treatment process of the red mud (bauxite residue). Also, scrutinize the biodegradation of heavy metals in red mud.

Deliberate Decisions in Product Design: An Approach Inspired by Life Cycle Assessment Techniques

In the wide field of product design, challenges often not just lie in making decisions but in determining what decisions truly merit attention and consideration. This paper explores an approach that makes techniques used in Life Cycle Assessments (LCA) instrumental in assessing and collating decisions and the significance of deciding. Where well-established LCA methodologies are recognised for evaluating environmental impacts, it is postulated that their underlying techniques can be employed to actively support and enrich decision-making in earlier stages of product design and development processes.This paper proposes an approach that uses LCA techniques to disentangle the cohesion and relations between the myriad decisions in design and development processes, thus aiming to identify the predominant, influential, sensitive, precarious, and consequential decisions. With that, the approach aims to support and enrich decisions making, not (just) by aiming for the best decision outcome, but foremost by addressing the most impactful decisions. The aim is to allow designers to navigate decisions and deliberations of decision processes within the context of the overall design process, while correlating the exactitude of a decision outcome to the impact and sensitivity that decisions have in the evolvement of the development trajectory.

Gate-to-gate approach in life cycle assessment of steel pipe products

The iron and steel industry is of great strategic importance in Indonesia and is expected to continue its growth. One sector within this industry that is projected to see increased production is the steel pipe manufacturing sector. However, the significant emissions produced during steel pipe production have prompted many countries to seek ways to reduce these emissions. One such method is the implementation of the Life Cycle Assessment (LCA) technique, which assesses the environmental impact and serves as a valuable decision-making tool alongside other important methods. This approach aims to enhance sustainable production and consumption effectively and efficiently. In a specific case study, two types of steel pipe products were analyzed using LCA, focusing on various stages from material transportation to steel pipe production and delivery. The study assessed the environmental impact using the midpoint impact category, which includes factors like Global Warming Potential (GWP), Photochemical Ozone Creation Potential (POCP), Acidification Potential (AP), and Ecological Toxicity Potential (ETP) using the CML2001 characterization methods. The findings revealed that the production of SAWH pipes has a higher environmental impact in terms of GWP, AP, and POCP compared to HFW pipes, while HFW pipes had a greater ETP value. GWP was identified as the most significant potential impact for both products, with the pipeline production stage contributing the most to GWP. Overall, these results offer an initial understanding of the sustainability levels of steel pipe production, serving as a foundation for the development of pollution control measures to maintain the competitiveness of the industrial plant.

Resource and Greenhouse Gas Reduction Effects through Recycling of Platinum-Containing Waste

When disposing of waste metal resources in landfills, environmental issues such as soil contamination may arise. Recycling these resources not only recovers valuable metals but also mitigates environmental pollution. Platinum (Pt), a valuable metal used in fuel cells for its high water production activity, will see increased future demand as a fossil fuel alternative. This study analyzes the environmental and resource reduction effects of recycled Pt, considering the growing emphasis on its recycling for stable supply and demand of Pt. The environmental impact and resource consumption of recycled Pt with primary Pt (from natural mines) were compared and analyzed using the Life Cycle Assessment technique. The results revealed that resource consumption for primary Pt was 8.25 × 101 kg Sb-eq./kg, significantly more than the 5.45 × 100 kg Sb-eq./kg for recycled Pt. This represents an environmental reduction effect of approximately 93%. In the case of greenhouse gas emissions, primary Pt emitted 1.35 × 104 kg CO2-eq./kg, while recycled Pt emitted 6.94 × 102 kg CO2-eq./kg, resulting in an environmental reduction effect of approximately 95%. In conclusion, recycling Pt, compared to primary extraction, offers substantial environmental and resource reduction benefits. This study underscores the significance of recycling and highlights the potential environmental improvements achievable through sustainable practices.

A hybrid model of self-organizing map and deep learning with genetic algorithm for managing energy consumption in public buildings

Intelligent applications have become essential for effectively managing energy consumption in public buildings. Energy management, especially for critical infrastructure buildings and public buildings, often serves as an example of sustainable practices since it tries to increase energy resilience through backup power systems. These structures were dealing with more difficulties, such as tight budgets, complicated regulations, behavioral issues, and others. Managing energy consumption in buildings is a multifaceted undertaking that entails grappling with non-uniform energy usage and a lack of established design guidelines for implementing energy-efficient and sustainable solutions. Consequently, analyzing energy utilization trends in public buildings and projecting future energy requirements is of paramount importance. This comprehension is indispensable to the identification and acknowledgment of energy consumption patterns in commercial and institutional buildings. Our research is highly significant for optimizing energy usage, reducing environmental impact, and making informed decisions in building management. We emphasize the importance of our predictive capabilities, sustainability, and life cycle assessment (LCA) techniques in achieving these objectives. Our hybrid model improves energy efficiency while also making a significant contribution to the larger objective of meeting sustainability standards in the built environment through thorough analyses of real-world data. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. In order to determine the number of energy consumption pattern clusters, a Self-Organizing Map (SOM) model was utilized in conjunction with Principal Component Analysis (PCA). The determination of clustering levels for each structure was executed by utilizing K-means in conjunction with a Genetic algorithm (GA). This approach enabled the identification of optimal clustering levels for the given structures, thereby facilitating effective analysis and interpretation of the data. The GA was specifically employed to determine the optimal centroid points for each cluster, thus optimizing the performance of the fitting model. Cluster analysis pattern extraction has made determining which buildings consume the most energy easier. As intelligent models for predicting energy usage, Convolutional Neural Networks (CNNs) and CNNs paired with a GA have also been used. The application of a GA to modify multiple settings of a CNN was conducted at this stage. The CNN model using a GA shows the method that was used provides higher accuracy and standard error compared to the conventional approach. It achieves a 94.01% accuracy on the training dataset and a 93.74% accuracy on the validation dataset with an error rate of 0.24 and 0.26 respectively. On the training dataset, the accuracy is 89.03% with a standard error of 0.3, whereas on the validation dataset, the accuracy is 88.91% with a 0.33 standard error. This research can help policymakers in the energy sector make better decisions regarding the timing of energy supply and demand for public buildings.