Efficient Energy Management System Research Articles

Social factors influence on energy efficiency management and development of renewable energy sources

Current issues of increasing energy efficiency in the conditions of new industrialization are considered. The article analyzes social factors that affect the effectiveness of energy efficiency management and the development of renewable energy sources, including social awareness of the population, state support, the role of business and social responsibility of enterprises. The study shows that social aspects play a critical role in the implementation of energy-saving technologies and the achievement of sustainable development goals. A comprehensive approach was used, which includes theoretical analysis, questionnaires, expert interviews and statistical analysis to identify key social factors and their impact on energy efficiency management. The results of the study can be useful for developing recommendations for improving energy efficiency management systems taking into account social aspects, as well as for forming a strategy for the development of renewable energy sources in Ukraine and a hydrogen strategy.In the process of researching the impact of social factors on energy efficiency management and the development of renewable energy sources, several unresolved aspects were identified that require additional analysis. Firstly, the insufficiency of research that would reveal the relationship between the social awareness of the population regarding energy efficiency and the level of implementation of energysaving technologies and energy storage technologies using hydrogen was revealed.Sociocultural factors such as educational attainment, cultural stereotypes and values remain understudied. Secondly, the role of social responsibility of business in the implementation of energy-efficient solutions needs a deeper study, since questions about the impact of social initiatives on these processes still remain open. Thirdly, the study of the impact of public policy on the social aspects of energy efficiency is also an underdeveloped direction, particularly in the context of how public programs take into account social factors, such as the adoption of new technologies by the population and the impact on social groups with different income levels.And fourthly, how does Ukraine maintain the level of awareness and understanding of hydrogen technologies. These aspects require further scientific analysis to improve the efficiency of energy efficiency management and the development of renewable energy sources.

MIWO based MPPT of PV system for induction motor driven water pumping system

Renewable energy source-based Water Pumping Systems (WPSs) have gained popularity for drinking, agricultural, and industrial purposes. Locally situated standalone Photovoltaic (PV) powered WPS can provide a viable water supply solution, especially with induction motors (IM) due to their advantages over other motor types. However, solar PV energy output is greatly influenced by weather conditions, necessitating energy storage systems like batteries, which increase costs and require maintenance. This research proposes an efficient energy management system for a PV-powered IM-driven WPS that does not rely on batteries. The system handles Partial Shading Conditions (PSC) effectively, thanks to a sensorless speed controller combining a Modified Invasive Weed Optimization (MIWO) mechanism with the Perturb and Observe (P&O) method. This controller uses the inverter as an MPPT circuit, eliminating the need for a distinct converter to follow the MPP. The hybrid P&O-MIWO method's outcomes are compared with those of P&O hybridization with GA, PSO, and GWO to assess efficiency under various PSCs. TS-Fuzzy controllers are employed for voltage control at the DC-link and motor speed. The proposed system is analyzed across different scenarios and validated using a Simulink model in MATLAB. Results indicate that the hybrid P&O-MIWO mechanism enhances energy generation from the PV unit without relying on energy storage devices, providing a cost-effective solution for PV-based WPS implementation.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING DROOP CONTROL TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using the droop control technique. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing an efficient energy management system for a small-scale hybrid wind-solar-battery-based microgrid to extract maximum profit from electricity generation. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

Optimized Energy Management System for Wind Lens-Enhanced PMSG Utilizing Zeta Converter and Advanced MPPT Control Strategies

This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system.

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

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The national system of energy saving management in budget sector organizations is presented through the prism of the implementation of targeted directions for increasing the level of energy efficiency by the program-target method, the purpose of which is the concentration of resources of business entities and the construction of a unified energy efficiency management system and the coordination of efforts of both state and regional authorities, local self-government, institutions,etc. An important role is given to the components of the program-target method as a mechanism of state management of a unified system of state and regional authorities and local self-government bodies, institutions, etc., which, in particular, will ensure the coordination of energy components and the formation of an acceptable price for educational services. According to the results of the research, the need for the formation of an energy-efficient policy in each institution of higher education, despite its regional location, based on a comprehensive approach from the standpoint of a single energy space, has been actualized. The author considers the management of energy saving projects of the budget sector organization in relation to their energy security system, i.e. the management system of a budget institution from the standpoint of a single energy space, which provides an effective, reliable and ecologically safe environment, which is minimally affected by internal and external factors of energy saving, and is the guarantor of the quality of services, in particular energy services, provided to the population by public sector facilities. The main factors of energy efficiency of budget sector organizations, the procedure for implementing the energy saving and energy efficiency improvement program, the main factors of managing energy efficient projects in the budget sector, analytical tools for modeling their evaluation system and the stages of developing a scenario of an innovative model of their management are presented. The proposed technologies and tools can be used to build an innovative management model of energy saving projects of budget organizations, in particular, using the pyramid-aggregate modeling method.

Wind power forecasting using a GRU attention model for efficient energy management systems

Wind power forecasting using a GRU attention model for efficient energy management systems

Multi-Layer Energy Management and Strategy Learning for Microgrids: A Proximal Policy Optimization Approach

An efficient energy management system (EMS) enhances microgrid performance in terms of stability, safety, and economy. Traditional centralized or decentralized energy management systems are unable to meet the increasing demands for autonomous decision-making, privacy protection, global optimization, and rapid collaboration simultaneously. This paper proposes a hierarchical multi-layer EMS for microgrid, comprising supply layer, demand layer, and neutral scheduling layer. Additionally, common mathematical optimization methods struggle with microgrid scheduling decision problem due to challenges in mechanism modeling, supply–demand uncertainty, and high real-time and autonomy requirements. Therefore, an improved proximal policy optimization (PPO) approach is proposed for the multi-layer EMS. Specifically, in the centrally managed supply layer, a centralized PPO algorithm is utilized to determine the optimal power generation strategy. In the decentralized demand layer, an auction market is established, and multi-agent proximal policy optimization (MAPPO) algorithm with an action-guidance-based mechanism is employed for each consumer, to implement individual auction strategy. The neutral scheduling layer interacts with other layers, manages information, and protects participant privacy. Numerical results validate the effectiveness of the proposed multi-layer EMS framework and the PPO-based optimization methods.

OPTIMAL CONTROL OF A HYBRID PV-WIND POWER SYSTEM FOR ISLANDED HYBRID MICROGRID USING MOGA-FUZZY LOGIC CONTROLLER

The paper presents a comprehensive approach to managing the complexities associated with the integration of photovoltaic (PV) and wind energy systems in islanded microgrid environments. These microgrids, often isolated from the main power grid, face unique challenges due to the variability and intermittency of renewable energy sources, which can lead to instability and inefficiencies in power supply. To address these challenges, the study introduces a novel control strategy that combines a Multi-Objective Genetic Algorithm (MOGA) with a Fuzzy Logic Controller. The MOGA is utilized to explore a broad solution space and identify optimal control parameters that balance multiple objectives, including minimizing power fluctuations, maximizing the utilization of renewable energy, and ensuring a stable and reliable power supply. This optimization is crucial in islanded microgrids, where the lack of a larger grid connection necessitates highly efficient and responsive energy management systems to maintain stability. The Fuzzy Logic Controller, on the other hand, provides a flexible and adaptive control mechanism that responds to the dynamic and often unpredictable nature of renewable energy generation. By interpreting input variables in a way that mimics human decision-making, the Fuzzy Logic Controller can effectively handle the inherent uncertainties and non-linearities in the power system, adjusting the operation of the PV and wind power sources, as well as any supplementary energy storage systems, to optimize performance. This adaptive capability is particularly beneficial in scenarios where rapid changes in weather conditions can significantly impact energy generation and consumption patterns. The integration of MOGA with Fuzzy Logic not only enhances the decisionmaking process but also allows for the simultaneous consideration of multiple objectives, which is a critical advancement over traditional single-objective optimization techniques. This dual approach ensures that the hybrid PV-wind power system operates at its highest efficiency, balancing the need for sustainability with the practical requirements of reliability and economic viability. The results from extensive simulations, which model various operational scenarios and disturbances, demonstrate that the proposed MOGA-Fuzzy Logic Controller significantly improves the stability and efficiency of the islanded microgrid. The system is shown to effectively manage power flows, reduce dependency on fossil fuel-based backup generators, and increase the overall penetration of renewable energy. The proposed MOGA-Fuzzy Logic Controller stands out as a promising solution for the optimal control of hybrid PV-wind power systems, offering a viable pathway for achieving sustainable energy goals in islanded and other decentralized grid settings. This work not only advances academic knowledge but also has practical implications for the design and operation of future energy systems, making it a significant contribution to the field of renewable energy and power system engineering.

Optimization of Fuel Consumption by Controlling the Load Distribution between Engines in an LNG Ship Electric Propulsion Plant

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction.

Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently.

Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture

Today, optimization is crucial to solving energy crises, especially in smart homes. However, the optimization-based methods for energy management in smart agriculture available globally need further improvement, which motivates this study. To resolve the problem, an efficient scheduling farm energy management system is required. Therefore, this study proposes a Farm Energy Management System (FEMS) for smart agriculture by adopting a honey-badger optimization algorithm. In the proposed system, a multi-objective optimization problem is formulated to find the best solutions for achieving the set of objectives, such as electricity cost, load minimization and peak-to-average ratio minimization, while considering the farmers' comfort. The proposed system considers commercialized agriculture with the integration of Renewable Energy Resources (RES). Also, the proposed system minimizes both load consumption and electricity costs via the scheduling of farm appliances in response to Real-Time Pricing (RTP) and Time-of-Use (ToU) pricing schemes in the electricity market. Extensive experiments are carried out in MATLAB 2018A to determine the efficacy of the proposed system. The proposed FEMS consists of sixteen farm appliances with their respective power ratings, inclusive of RES. The simulation results showed that a system without FEMS has a high electricity cost of 50.69% as compared to 43.04% for FEMS without RES and 6.27% for FEMS with RES when considering the ToU market price. For RTP market price, a system without FEMS has an electricity cost of 42.30%, as compared to 30.64% for FEMS without RES and 27.24% for FEMS with RES. Besides, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 151.40 kW for FEMS without RES and 18.85 kW for FEMS with RES when considering the ToU market price. Also, for the RTP market price, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 186.40 kW for FEMS without RES and 90.68 kW for FEMS with RES. The significance of the study is to propose a conceptualized FEMS based on the honey badger optimization algorithm. The proposed system provides scheduling of farm appliances that alleviates the burden of the electricity grid and is cost-effective for large and small-scale farmers.

Optimal power-split of hybrid energy storage system using Pontryagin’s minimum principle and deep reinforcement learning approach for electric vehicle application

The battery supercapacitor hybrid energy storage system (HESS) based electric vehicles (EVs) require an efficient online energy management system (EMS) to enhance the battery life. The indirect optimal control method, like Pontryagin’s minimum principle (PMP), is gaining attention for its inherent instantaneous optimization and computational simplicity. However, determining the costate, non-casual variable of PMP for a given driving scenario is challenging and significantly affects the optimal solution of minimizing the battery degradation. In this study, we propose an online hybrid EMS by combining PMP and deep reinforcement learning (RL) to precisely predict the optimal costate and simultaneously minimize the battery degradation in a battery supercapacitor HESS assisted EV. Also, we propose an analytical approach to estimate the initial costate to create the costate action space of RL, which could also be used to estimate the initial costate guess for similar applications. We validate the performance of the proposed EMS under driving cycle uncertainties and untrained driving cycles via simulation. The results demonstrate the effectiveness of deep RL for optimal costate estimation to satisfy the charge sustainability of the supercapacitor. The predicted costate maintains the supercapacitor state-of-charge in the desired range while minimizing battery current for uncertain driving profiles. Further, we have compared the battery energy depletion in the proposed EMS and an offline optimal EMS to study the feasibility of the proposed intelligent EMS. The proposed EMS outperforms the offline EMS, achieving a significant improvement of 900 charge–discharge cycles.

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

The rapid integration of renewable energy sources (RES) and the electrification of transportation have significantly transformed modern energy infrastructures, emphasizing the need for efficient and flexible energy management systems. This study presents an intelligent, variable-fed, Type-2 Fuzzy Logic Controller (IT2FLC) designed for optimal management of Hybrid Microgrid (HMG) energy systems, specifically considering different modes of Electric Vehicles (EVs) integration. The necessity of this study arises from the challenges posed by fluctuating renewable energy outputs and the uncoordinated charging practices of EVs, which can lead to grid instability and increased operational costs. The proposed IT2FLC is based on comprehensive mathematical modeling that captures complex interactions among HMG components, including Doubly Fed Induction Generator (DFIG) units, photovoltaic (PV) systems, utility AC power, and EV batteries. Utilizing a yearly dataset for simulation, this work examines the HMG’s flexibility and adaptability under dynamic conditions managed by the proposed intelligent controller. A Simulink-based model is built for this study to replicate the dynamical operation of the HMG and test the precise and real-time decision-making capability of the proposed IT2FLC. The results demonstrate the IT2FLC’s superior performance, achieving a substantial cost avoidance of nearly $3,750,000 and efficient energy balance, affirming its potential to sustain optimal energy utilization under stochastic conditions. Additionally, the results attest that the proposed IT2FLC significantly enhances the resilience and economic feasibility of hybrid microgrids, achieving a balanced energy exchange with the utility grid and efficient utilization of EV batteries, proving to be a superior solution for optimal operation of hybrid grids.

Opposing approaches for enhancing energy efficiency in buildings

Abstract This paper proposes two opposing approaches for enhancing energy efficiency in buildings as a part of combating climate change and summarizes interim outcomes from two ongoing research projects. The first project focuses on adopting sustainable design methodologies in new buildings, and the second one aims at ’greening’ old but otherwise economically viable and usable buildings. Finally, the paper points to the potential for an integrated platform for efficient energy management system for buildings for clients and/or owners, albeit with addressing a range of issues.

An SDP relaxation in the complex domain for the efficient coordination of BESS and DGs in single-phase distribution grids while considering reactive power capabilities

This research proposes an efficient energy management system design for medium-voltage distribution networks from a perspective of convex optimization. The exact nonlinear programming problem that represents the operation of distributed energy resources (DERs), such as batteries and photovoltaic sources, is approximated as an equivalent semi-definite programming (SDP) problem. This SDP approximation is achieved in the complex domain using Hermitian matrices, which are the equivalent of positive semi-definite matrices in the real number domain. Two test feeders, one with 27 nodes and the other with 33 nodes, are modified to match the characteristics of Colombian rural and urban networks. The 27-bus grid is adapted to accommodate the average demand and solar generation conditions of the municipality of Capurganá, while those of the city of Medellín are used for the 33-bus grid. One of the main contributions of this research is that it considers a DER operation with variable power factors, which allows for significant improvements compared to the traditional use of a unitary power factor scheme. Numerical comparisons are conducted using metaheuristic optimizers, including parallel versions of the particle swarm optimizer, the vortex search algorithm, and the continuous version of the genetic algorithm. These comparisons demonstrate the effectiveness of the proposed SDP approximation. All simulations are carried out in the MATLAB software (version 2022b), using the disciplined convex optimizer (CVX) and the SDPT3 and SEDUMI solvers.

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.

Evaluation of Deep Learning-Based Non-Intrusive Thermal Load Monitoring

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

The development of energy saving measures as a basis of ensuring energy efficiency in higher education institutions in healthcare of Ukraine

The implementation of efficient energy management systems (EnMS) to meet the requirements of ISO 50001 is an urgent problem, taking into account the aggravation of the issue of energy supply and energy saving in the country, especially in the conditions of martial law. The main issue facing higher education institutions (HEIs) of Ukraine when creating rational activities for the use of energy resources is the implementation of an energy management system to meet the requirements of DSTU ISO 50001:2020. The main problems faced by all HEIs now are revision of legislation, rising prices and cost of energy resources, high inflation, frequent exchange rate changes. EnMS will allow each institution to monitor energy consumption and provide an opportunity to develop and implement effective energy saving projects. Aim. To theoretically substantiate and propose practical energy saving measures for a higher education institution (HEI) in the healthcare sector of Ukraine within the framework of the implemented energy management system. Materials and methods. The research materials were data from scientific publications, statistical reports and the results of own research, in particular measures for the energy modernization of electric lighting in public areas of educational buildings of HEI. Such scientific methods as the method of content analysis, comparative analysis, the method of generalization and systematization of information, as well as the methods of mathematical and statistical calculations were applied. Results and discussion. The calculation of electricity costs and savings for lighting public areas of all educational buildings of HEI of healthcare of Ukraine has been carried out, and the need for lighting equipment has been formed. The total electricity savings during the energy modernization of lighting in educational buildings of HEI of healthcare of Ukraine and the total economic efficiency from the electricity savings have been calculated. It has been found that the energy efficiency during the energy modernization of lighting in the educational buildings of HEI is 25,126 kWh/year, and the total economic efficiency from electricity savings is 69,600 UAH/year. Conclusions. Energy saving measures have been proposed as part of the implemented energy management system, which will make it possible to significantly save electricity and reduce the financial costs for its purchase and distribution. This, in turn, will allow HEI to form its own general energy supply strategy aimed at improving both energy efficiency and overall operational efficiency.