Opportunities For Energy Savings Research Articles

Dual-Module humidity pump with hollow fiber membranes for isothermal dehumidification in industrial drying

Traditional dehumidification and drying processes are energy-intensive as they involve cooling the air below the dew point to condense and remove water vapor. Vacuum membrane dehumidification offers energy-saving opportunities, but its full potential remains undeveloped due to the presence of water vapor within the vacuum pump and excessively high pressure ratios. Recent studies proposed a dual-module humidity pump (DMHP) to address this, but the increased air permeation into the system requires strategies to prevent air pressure buildup and diffusion barrier formation. This study investigates a DMHP, specifically designed to address these issues by incorporating hollow fiber membranes for isothermal dehumidification in industrial heat pump dryers (HPDs). Membrane geometry and properties are coupled with a partial pressure-driven ε-NTU method, and a discretized model is used to identify water vapor transport under sub-ambient conditions. Thermodynamic models of the components are developed, and membrane-integrated HPDs are compared to a conventional HPD. The proposed system’s specific moisture extraction rate (SMER) exceeds conventional HPDs by 69%. Global sensitivity analysis reveals that SMER is 7.2 times more responsive to ambient conditions than dryer inlet conditions, with membrane geometry and properties’ interactions exerting greater influence than their individual effects. The optimum pressure ratio for the water vapor compressor, ranging from 1.2 to 3.8 and adjustable via synchronized control of rotational speeds with the vacuum pump, enhances SMER by up to 33.7% with a vapor balance ratio of 0.84–0.89. The results suggest that future work should investigate further optimization of membrane modules and variable built-in volume ratio compressors to unlock the full potential of DMHP technology.

Technical & Economic Feasibility Study of Proposed Pump Storage Power Plants at Kuda Oya, Mul Oya, Gurugal Oya, and Dambagasthalawa

This paper presents a Technical and Economic Feasibility Study of proposed Pumped Storage Power Plants (PSPPs) at KM (Kuda Oya, Mul Oya), KMG (Kuda Oya, Mul Oya, Gurugal Oya), KG (Kuda Oya, Gurugal Oya), and Dambagasthalawa. Sri Lanka aims to transition away from a coal-dominant electricity sector within the next decade, aligning with sustainability goals outlined by the United Nations. In pursuit of affordable and clean energy sustainability, the Sri Lankan government has opted to shift its long-term energy policy towards renewable sources, departing from fossil fuels. Despite this shift, a significant portion of Sri Lanka's electricity consumption—approximately 600 MW from oil and 900 MW from coal—persists. The country now aims to source 80% of its energy from renewable sources, necessitating a focus on underutilized opportunities. In selecting the best candidate sites for Pumped Storage Power Plant (PSPP) design, comprehensive hydrological and sedimentation studies play a pivotal role. In this endeavor, we endeavor to re-rank the selected candidate sites for pumped storage power plants considering technical & financial feasibility by conducting comprehensive hydrological and sedimentation studies. These studies are pivotal for assessing the feasibility and long-term viability of potential sites, aiming to select the most sustainable candidate site in every aspect. Therefore, the primary objective of this research is to conduct a thorough hydrologic study of the proposed KMG and Dambagastalawa pump storage power plants, focusing on identifying potential energy-saving opportunities in pumping when these plants operate as open-loop systems. This study will analyze the hydrology of the upper ponds of the Kuda Oya, Mul Oya, and Gurugal Oya (KMG) pump storage power plants, and the Dambagastalawa Oya pump storage power plant, assessing the potential reduction in pumping energy. Such insights are essential for optimizing the design and operation of PSPPs to ensure economic viability and sustainable energy generation. Furthermore, this research addresses the importance of studying sedimentation in both the upper and lower ponds of the KMG and Dambagastalawa pump storage power plants. Sedimentation calculations will be performed to determine reservoir lifetimes, offering critical insights into the long-term feasibility and maintenance requirements of these projects. The findings of this study are expected to provide valuable guidance to policymakers, investors, and PSPP designers in selecting the most suitable sites for addressing Sri Lanka's peak power demands and urgent electricity needs. By emphasizing the significance of hydrological and sedimentation studies, this research underscores the importance of thorough site assessment in optimizing the design and operation of Pumped Storage Power Plants.

Energy Awareness, Energy Use, and Energy-Saving Opportunities in the Caribbean: The Island Curaçao as a Case Study

Energy Awareness, Energy Use, and Energy-Saving Opportunities in the Caribbean: The Island Curaçao as a Case Study

Potential for Electrical Energy Savings in AC Systems by Utilizing Exhaust Heat from Outdoor Unit

This study explores the potential of utilizing waste heat from air conditioning systems, one of the largest consumers of electrical energy. Currently, most of the waste heat generated by outdoor units is typically released into the environment without being utilized, leading to missed energy-saving opportunities. This study analyzes the potential for improving electrical energy efficiency in air conditioning (AC) systems by harnessing this waste heat. Two primary approaches are evaluated: the first is the use of waste heat for domestic water heating, and the second is the conversion of heat into electrical energy using thermoelectric generators (TEG). The results of this research indicate that both methods have the potential to improve overall energy efficiency significantly. However, challenges related to conversion efficiency and integration of these technologies with AC systems require further, more specific studies. These findings are expected to contribute to more efficient and environmentally friendly cooling systems by optimizing technology and overcoming barriers to wider implementation.

Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.

Energy saving potential in steam systems: A techno-economic analysis of a recycling pulp and paper mill industry in Morocco

The industrial sector represents nearly 40 % of global energy consumption, with steam systems accounting for 30 % of energy use in manufacturing. As the need for cleaner processes intensifies within African and global energy transitions, adopting energy efficiency practices becomes imperative. Governments are focusing on reducing energy losses in fossil fuel-dominated manufacturing industries, which are major producers of greenhouse gas emissions. Despite their significant energy consumption, industrial heat processes are often overlooked in efforts to reduce costs and emissions. To address this, the study examines thermal energy processes and identifies energy-saving opportunities within steam systems through a techno-economic analysis of a recycling pulp and paper mill in Morocco. This is the first study focusing specifically on steam systems within a particular industry in the country. The research uses a system approach covering steam generation, distribution, end-use, and recovery. The current system is benchmarked through a rating questionnaire, and proposed energy-saving actions include leak repair, pipe insulation, blowdown management, and improved boiler combustion efficiency. An economic study evaluates the cost-effectiveness of these opportunities. Findings reveal a 6 % reduction in thermal energy consumption, annual savings of $417 899 and nearly 482 TJ, a 6 % decrease in GHG emissions (2158.5 tons of CO2 per year), and water savings of approximately 5300 m³ per year (13 %). The total investment of $8750 has negligible payback periods. Despite diverse industrial activities, this work is useful for other industries since the fundamental components of the steam system are widely shared. Moreover, it supports the African Union's Agenda 2063 and United Nations Sustainable Development Goals by providing practical insights and recommendations for enhancing industrial energy efficiency.

Equilibrium modelling of steam gasification of PKS system and CO2 sorption using CaO: A digitalized parametric and techno-economic analysis

The conversion of palm oil waste into energy can complement the energy mix with renewable energy and bring economic benefits to the palm oil industry. A process simulation model for palm kernel shell (PKS) steam gasification for H2-enriched syngas with the integration of CO2 capturing using CaO has been investigated using Aspen Plus V10®. Techno-economic and energy analyses have also been conducted to identify energy-saving opportunities for commercialization. The effect of process variables, including reactor temperature (600–800 °C), Steam/PKS ratio (0.5–2 wt/wt), and CaO/PKS ratio (0–1.5 wt/wt), have been determined, with the predicted results compared to the reported experimental data. H2 concentration has been increased with 76–78 vol% with the temperature elevation from 650 to 750 °C. Additionally, a substantial increase in H2 content from 68 to 72vol% was observed when the steam flow rate was increased from 0.5 to 1.5. Conversely, the CO2 concentration decreased from 25 to 8vol% as the adsorbent ratio was raised from 0.5 to 1.5. The techno-economic analysis showed that the capital investment is $4.11 million, and the operating cost is $3.89 million per year, which is also very high due to the high raw material costs. In the case of energy, saving that 3.03 and 1.513 Gcal/hr can be saved in terms of utilities and gas cooling that economized the process which shows that utilization of these in heat exchange networks can significantly reduce costs, with up to 78 % savings in heat exchanger capital and 35 % in total energy consumption. The energy potential could be harnessed through the digitalization of the process.

Unlocking Energy Efficiency: Debunking Myths on the Road to Decarbonization

Energy efficiency is widely recognized as the foundational and most critical strategy for decarbonizing the manufacturing sector. Misconceptions surrounding energy efficiency measures often hinder their widespread adoption. This article aims to debunk five common myths and provides data and resources to help implement efficiency projects faster and more effectively to achieve greater decarbonization. First, the article challenges the myth that organizations have exhausted all possible energy efficiency opportunities by achieving voluntary energy intensity goals or energy performance certification. Second, it also addresses the misconceptions that efficiency projects are capital-intensive, require many qualified specialists, and have long investment return periods. By presenting real-world case studies and referencing commonly found efficiency opportunities, the article illustrates that energy-savings opportunities are ubiquitous. Organizations can use various contracting mechanisms as well as financial and technical resources from utility companies and government programs to lessen their burden. The notion that efficiency measures can be implemented solely in proprietorship facilities is dispelled. This article emphasizes the importance of green leases and explains that aligning decarbonization goals between the lessor and lessee can help drive savings for both parties. Finally, using unbundled renewable energy certificates as the sole pathway to decarbonization is strongly discouraged. By debunking these prevalent myths, this article aims to foster a deeper understanding of energy efficiency’s potential as a cornerstone of decarbonization efforts and to embrace it as a critical pathway toward a sustainable future.

Doing More with Less: Applying Low-Frequency Energy Data to Define Thermal Performance of House Units and Energy-Saving Opportunities

Doing More with Less: Applying Low-Frequency Energy Data to Define Thermal Performance of House Units and Energy-Saving Opportunities

Productivity enhancement of continuous miners packages of underground mines with energy conservation in industrial sectors

Energy auditing is crucial for both emerging and developed nations, focusing on aspects such as energy efficiency, quality and intensity. This becomes especially significant in the industrial sector, where major operating costs involve materials, machinery, personnel and energy. A key objective is identifying energy consumption in the Continuous Miner Machine, boasting a 1050 KW connected load, to explore opportunities for energy savings and improved quality. The execution of an energy audit promises increased efficiency, enhanced power quality, reduced costs and prevention of energy wastage. This research study has meticulously examined the electrical equipment supplying power to Continuous Miner Machines, proposing modifications to boost production through energy-efficient methods. Comprehensive assessments of primary substations, including Incomers, Transformers, Feeders, etc., involve tests for voltage, current, power factor, harmonics and AC waveform. Thermal imaging is employed to analyze the operational temperature of the electrical equipment. The graphical representations of test outcomes have highlighted a significant recommendation: installing an Automatic Power Factor Controller on the inductive load side of transformers could lead to a notable 3.85% reduction in energy costs.

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.

(Invited) Advancement in Electrosynthesis of Fuels and Chemicals Using H2O and/or CO2 and N2 at Intermediate Temperatures at Idaho National Laboratory

Fuels and commodity chemicals make up a significant share of the potential energy savings opportunities for decarbonized economy and society. New innovative technologies are needed that can reduce the amount of energy and carbon intensity currently required to produce the key fuels and chemicals or associated building blocks. Electrochemistry is playing an increasingly important role in manufacturing as we reduce energy use and address uncertain supply chains driven by industrial electrification. In comparison to processes driven by heat and pressure, electrochemically driven synthesis of fuels and chemicals provides unique opportunities for selective reactions and separations offering increased flexibility to manufacturing. As one of DOE’s premier multi-program energy research laboratories, Idaho National Laboratory’s (INL) mission is to ensure the nation’s energy security with safe, competitive and sustainable energy systems and unique national and homeland security capabilities. INL’s initiative, integrated energy systems (IES) provides carbon-free nuclear and renewable energy for downstream use in manufacturing and transportation. The manufacturing component of IES focuses on integrating clean, carbon-free electrons with waste/renewable heat and includes both manufacturing of electrochemical devices and implementing them in energy-efficient processes. In this talk, we will provide representative examples for the pillar of energy to molecules and materials (E2M2) by utilizing the protonic ceramic electrochemical cells/membrane reactors (PCEC/PCEMR). The electrochemical route using PCEC/PCEMR offers a solution of process intensification and low carbon intensity. We will also share our cost perspective when compared to other lower and higher temperature counterparts.

A composite indicator-based energy-efficiency benchmarking for residential buildings

PurposeBuildings require significant energy, and meeting energy demands is becoming exceedingly challenging. Energy demand reduction goals are now prioritised as the demand is rising. Energy-saving improvements and opportunities can be provided if enough information is provided through building energy benchmarking. The study focuses on developing a framework for benchmarking the energy efficiency of residential buildings.Design/methodology/approachThis study applied multiple linear regression analysis to analyse the energy use of residential buildings and establish energy benchmarks. Over 2000 data from Jaipur city were surveyed, and regression analysis was done on 1527 datasets after fundamental statistical analysis. The research considered the significant energy used by household appliances and placed a greater emphasis on end-use appliances.FindingsThe comparison of the developed framework with the standard rating plan was carried out to evaluate the accuracy of the benchmarks. The validation of the model determines the gap between the predicted and actual value of the building energy. The recommendations were made for organisations and policymakers to employ multiple or combinations of methods to assess the reliability of the developed benchmark framework.Practical implicationsPolicymakers may promote awareness campaigns encouraging homeowners to consume less energy and make buildings more energy efficient. This technique may be applied worldwide with the proper and suitable adjustments and information provided.Originality/valueTo our knowledge, India needs residential building energy benchmarking framework studies. In addition, a new framework based on Composite Indicators was implemented to overcome the scepticism of the EPI/BPI or floor-based approach held by several academics and to offer energy benchmarking for residential buildings.

OcAPO: Fine-grained occupancy-aware, empirically-driven PDC control in open-plan, shared workspaces

Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs significantly, depending on occupancy levels, from that measured by the ceiling-mounted thermal sensors that drive the PDC control loop, (b) sparsely deployed sensors are unable to capture the often-significant differences in ambient temperature across neighboring zones. Using extensive real-world coarser-grained measurement data (collected over 60 days under varying occupancy conditions), (a) we first uncover the various parameters that affect the occupant-level ambient temperature, and then (b) devise a trace-based model that helps identify the optimum combination of PDC setpoints, collectively across multiple zones, while accommodating variations in the occupancy levels and weather conditions. Using this trace-based model, our OcAPO system can assure ambient temperature experienced by occupants within a tolerance of 0.3°C. In contrast, the existing approach of occupancy-agnostic, rule-based setpoint control violates this tolerance interval more than 80% of the time. However, this initial model requires unnecessary and continual database lookups and is unable to derive finer-grained setpoints, thereby potentially missing opportunities for additional energy savings. We thus collected data for another 15 days, with finer-grained setpoint control in increments of 0.2∘ under varying occupancy conditions in the second phase. To determine PDC setpoints efficiently, we subsequently used the empirical data to train a KNN-based regression model. Additional studies on our real-world testbed demonstrate the regressor-based OcAPO approach is able to assure occupant-level ambient temperature within a narrow 0.2°C tolerance. We also demonstrate that the regression version of OcAPO can reduce the opening percentage of PDC valves (an indirect proxy for energy consumption) by 58.9% under low occupancy compared to the trace-based model.

Harnessing Machine Learning to Optimize Renewable Energy Utilization in Waste Recycling

This research explores the application of Machine Learning techniques in utilizing renewable energy for the recycling process. As the world strives for sustainable solutions to meet energy needs and waste management challenges, this study investigates the integration of Machine Learning algorithms to optimize the production of renewable energy from waste recycling. By employing these algorithms, the research aims to enhance the efficiency and effectiveness of renewable energy generation while promoting environmentally responsible waste management practices. The study encompasses comprehensive data analysis from various recycling facilities, identifying energy consumption patterns and evaluating energy-saving opportunities. The findings reveal that applying Machine Learning can reduce energy consumption by up to 30%, increase recycling output, and decrease greenhouse gas emissions. These results highlight the potential benefits and challenges of implementing smart technology in the recycling process for renewable energy production. Furthermore, the research offers insights into how integrating Machine Learning can support long-term sustainability and significantly contribute to improved environmental management. Consequently, this study paves the way for a cleaner and more sustainable future, inspiring the broader adoption of innovative techniques within the waste management and renewable energy industries.

Electrical Energy Audit in Pratama Tapan Hospital

Hospitals, as large energy consumers, must adopt efficient energy-saving measures due to cost increases, regulations, and climate change concerns. In this study, an electrical energy audit was conducted at Pratama Tapan Hospital to identify energy-saving opportunities and recommend equipment replacements or technology upgrades. The primary objective was to determine the Energy Consumption Intensity (ECI) and analyze the potential for energy savings. The audit re-vealed an ECI value of 10.33 kWh/m²/year, highly efficient by ASEAN-USAID standards. However, 100% of rooms didn't meet the SNI 03-6197-2000 light intensity standard, causing discomfort. The study recommends installing specific light fixtures and adjusting lamp power to comply with the standard. These findings offer valuable insights for healthcare institutions striv-ing to achieve sustainability goals.

Closing the loop: does corporate sustainability capability matter for improving energy efficiency? Evidence from Pakistan

PurposeThis study aims to examine whether and how corporate sustainability capability influences energy efficiency through competitive intensity and slack resource availability.Design/methodology/approachThe authors applied a two-wave research design and administered a survey questionnaire to senior-level managers of 78 ISO-14001 and ISO-50001 certified manufacturing companies. The authors use a multi-method approach for data analysis. AMOS 23 software was applied for covariance-based structural equation modeling. In addition, SPSS 25 software was applied for hierarchical regression analysis to examine the causal relationships in the model.FindingsThe finding reveals that corporate sustainability capabilities, which include energy-saving opportunities, seizing energy-saving opportunities and resource reconfiguration, significantly improve firms’ energy efficiency. In addition, competitive intensity and slack resource availability positively moderated the relationship between corporate sustainability capability and energy efficiency.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine the link between corporate sustainability capability and energy efficiency in developing countries such as Pakistan. Although the influence of various corporate sustainability capabilities on sustainable performance has been widely examined in the literature, the role of corporate sustainability capability has been limitedly explored with energy efficiency. This study extends the literature by adding to the knowledge of corporate sustainability capability that enhances boundary conditions in developing countries.

A data mining-based framework to define optimal operation strategies for in-duct ultraviolet germicidal irradiation systems for different climatic conditions

This paper presents a novel data mining framework for optimizing the operation of in-duct Ultraviolet germicidal irradiation (UVGI) systems employed for air disinfection within air handling units. The performance of the UVGI system is influenced by the operating conditions of air handling units. As a result, under favourable operating conditions, the system produces excessive Ultraviolet C energy beyond the required disinfection levels, causing energy waste and accelerating the degradation of heating, ventilation, and air conditioning (HVAC) components. The framework aims to improve energy efficiency and mitigate material degradation within HVAC components by systematically adjusting UVGI system output based on changing operating conditions of air handling units throughout the year using advanced data mining techniques. The methodology involves several steps: Firstly, data pre-processing is performed to improve data quality. Then, the K-means clustering algorithm is applied to discover patterns in operational data. Subsequently, an effective performance index and baseline are defined to identify energy-saving opportunities within each cluster. Finally, the optimal operation strategy is defined based on the determined baseline. The framework is extended to five distinct climate zones to derive region-specific operation strategies. Results from a case study in a small office building in Montreal and an extension to five distinct climate zones in Canada demonstrate energy savings from 8% to 37%. This study offers a promising approach to optimize UVGI system operation, with potential applications in various energy systems in buildings requiring adaptive operational conditions for optimal performance.

Evaluation of Energy Performance Indicators and Energy Saving Opportunities for the Italian Rubber Manufacturing Industry

The objective of this work is the energy characterisation and evaluation of the energy efficiency potential of the rubber manufacturing industry in Italy, exploiting the detailed data included in energy audits by large and energy-intensive companies. This sector is divided into two sub-activities: the manufacture of rubber products and the production of tyres. Existing studies are focused mainly on tyre production, and there is a lack of quantitative evaluation of energy indicators that can provide guidance for improving process efficiency. In this work, updated global and specific energy performance indicators (EnPIs) related to the production process and to the auxiliary and general services are defined and evaluated. At the same time, targeted actions and interventions to improve the energy efficiency of the sector are analysed, showing the role of different intervention areas and their cost-effectiveness. The analysis is based on 100 Italian mandatory energy audits of the sector collected according to Art.8 EU Directive 27/2012. The applied methodology made it possible to calculate specific energy performance indicators by considering the overall and sub-process energy consumption of different production sites. Based on a detailed database containing real data from recent energy audits, this study provides an up-to-date and reliable benchmark for the rubber industry sector. In addition, the analysis of energy audits allows the identification of the most effective energy efficiency interventions for the rubber industry in terms of cost-effectiveness and payback time.

Chasing Comfort in the Chill: Simulating the Impact of Opened Streamlines on Microclimatic Conditions, Thermal Comfort and Building Facades in Cold Climate Cities

This project aims to evaluate the impact of water surfaces on urban microclimate change and the presence of water in dense urban areas on the heating and cooling loads of buildings by proposing the rehabilitation of urban water resources. It mainly questions whether favorable outcomes for urban microclimate may be expected at the local level with the rehabilitation of water bodies and the contribution of repaired water lines to energy savings in nearby buildings. The analyses are made for Erzurum, the coldest climate city in Türkiye, by using the ENVI-met program, a dynamic simulation tool widely used for microclimate analysis. The analyses use the meteorological data recorded on the site during the same years’ summer and winter periods. The microclimate change caused by the green areas, which involve the opening of covered streamlines, was estimated, and the change in the energy load of buildings was quantitatively analyzed. The findings show that rehabilitation of water lines in urban areas improved microclimate conditions and provided energy-saving opportunities for buildings. Thus, policy-makers should produce more green spaces, including water surfaces, in urban areas to improve the adaptation capacity of cities and mitigate the negative effects of climate change.