Minimize Energy Waste Research Articles

A multi-objective indirect neural adaptive processes control design for minimization of energy consumption: An experimental validation on a transesterification reactor

Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to solve the problem a multi-objective optimization that considers the minimization of the control energy can be adopted. In this paper, we propose to use a multi-objective indirect neural adaptive control concept for nonlinear systems with unknown dynamics. The control scheme consists of an adaptive instantaneous neural emulator and neural controller built on fully connected real-time recurrent learning networks (RTRL). The multi-objective particle swarm optimization (MOPSO) algorithm is used as a mechanism to find NE and NC adaptive learning rates that optimize the proposed objective functions: control energy minimization and closed-loop preservation. Comparative studies and experimental validation on a semi-batch reactor, used to generate cleaner biofuels, are performed to confirm the effectiveness of the proposed strategy.

Nitrogen-Fixing Gamma Proteobacteria Azotobacter vinelandii—A Blueprint for Nitrogen-Fixing Plants?

The availability of fixed nitrogen limits overall agricultural crop production worldwide. The so-called modern “green revolution” catalyzed by the widespread application of nitrogenous fertilizer has propelled global population growth. It has led to imbalances in global biogeochemical nitrogen cycling, resulting in a “nitrogen problem” that is growing at a similar trajectory to the “carbon problem”. As a result of the increasing imbalances in nitrogen cycling and additional environmental problems such as soil acidification, there is renewed and increasing interest in increasing the contributions of biological nitrogen fixation to reduce the inputs of nitrogenous fertilizers in agriculture. Interestingly, biological nitrogen fixation, or life’s ability to convert atmospheric dinitrogen to ammonia, is restricted to microbial life and not associated with any known eukaryotes. It is not clear why plants never evolved the ability to fix nitrogen and rather form associations with nitrogen-fixing microorganisms. Perhaps it is because of the large energy demand of the process, the oxygen sensitivity of the enzymatic apparatus, or simply failure to encounter the appropriate selective pressure. Whatever the reason, it is clear that this ability of crop plants, especially cereals, would transform modern agriculture once again. Successfully engineering plants will require creating an oxygen-free niche that can supply ample energy in a tightly regulated manner to minimize energy waste and ensure the ammonia produced is assimilated. Nitrogen-fixing aerobic bacteria can perhaps provide a blueprint for engineering nitrogen-fixing plants. This short review discusses the key features of robust nitrogen fixation in the model nitrogen-fixing aerobe, gamma proteobacteria Azotobacter vinelandii, in the context of the basic requirements for engineering nitrogen-fixing plants.

Thermal Integration in Sugar Production Using Pinch Analysis

Objective: the objective of this study is to conceptualize a network of heat exchangers designed to minimize energy waste and enhance the overall efficiency of the sugar production system.Methods: a systematic approach was adopted to analyze energy flows within the plant, identifying key areas for improvement, particularly in heating and evaporation processes. Heat accumulations in cascades and graphical analyses of composite curves were developed using specialized software to optimize heat exchange.Results: the results indicate a significant potential for energy savings, reducing the consumption of cooling and heating utilities in the plant by 7% and 30%, respectively. The developed computational tool allows for energy integration from simple processes to those with hundreds of streams. The pinch technology concept estimated an annual total savings of $464,850.08 in the selected process.Conclusion: this study demonstrates that thermal integration through pinch analysis not only improves energy efficiency in the sugar industry but also contributes to a considerable reduction in operational costs and environmental impact, providing a valuable tool for the industry’s sustainability and competitiveness.

OI url: https://doi.org/10.30574/wjarr.2024.23.3.2934

Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions.

Continuously Learning Prediction Models for Smart Domestic Hot Water Management

Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants’ behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance.

IoT based Smart Home Energy Tracker and Saver

The knowledge about the amount of energy used every month or day at home is the initial step to reduce the energy usage. So, the proposed research focusses on developing a smart energy management system using the IoT to track the daily electricity usage and enhance the energy efficiency at home. The system employs network of sensors placed throughout the home to monitor energy usage in real-time. The information gathered is transmitted to the Blynk Cloud and visualized through the Blynk app. The chart displayed in the Blynk app enables the user to identify the excessive energy usage and reduce both consumption and the electricity bills. By integrating IoT technology and user-friendly control interfaces, this research aims to provide homeowners with actionable insights to minimize energy waste, reduce utility bills, and contribute to environmental conservation.

Analysis of Renewable Source Self-Consumption Systems: Energy, Economic and Social Impact

This work analyzes renewable source self-consumption systems as an alternative to conventional fossil fuel sources for the residential, commercial, and industrial sectors. The proposed configuration is a hybrid array of individual or combined renewable energy sources like photovoltaic panels, wind turbines, hydroelectric micro-turbines, low enthalpy geothermal or biomass units, etc. The paper studies the energy balance for a prototype installation based on standard energy consumption and power supply from renewable sources, aiming to optimize the system performance by saving energy, increasing efficiency, and minimizing energy waste. The paper also analyzes the feasibility of the proposed configuration, its capacity for variable operating conditions adaptability, and the implementation factor in modern society. This work also evaluates the environmental impact of self-consumption systems powered by renewable sources in urban areas, which are deeply sensible to pollutant gasses emissions. The simulation analysis shows that this solution reduces greenhouse gas emissions and helps mitigate climate change by reverting the carbon dioxide balance in the atmosphere.

A Comprehensive Study on Net Zero Energy Buildings and Sustainable Construction Materials with Low Carbon Contents

One-third of the world's greenhouse gas emissions come from the building industry, with embodied carbon from the manufacture of building materials accounting for a sizable portion of these emissions. Prior research has mostly focused on examining the carbon emissions of low-rise structures, but it has failed to consider the significant influence that high-rise buildings have on a city's overall carbon emissions. Net-zero energy buildings (NZEBs) combine intelligent energy management strategies with conventional design knowledge to facilitate a sustainable and eco-friendly transition. The NZEB building revolution has the potential to become a long-term structural pillar for structures to come. The purpose of this study is to assess how various design factors and the embodied carbon in high-rise structures relate to one another. Additionally, the goal of this research is to keep annual consumer energy comfort in a home NZEB at its highest level without using any electricity from the electrical grid. In order to achieve long-term climate goals, our work emphasises the socio-economic advantages of adopting NZE buildings and further empowers consumer participation to minimise energy waste and carbon emissions. The findings provide a foundation for constructing high-rise structures in a more ecologically conscious manner to lower the structure's carbon emissions. The evaluation and comparison of the embedded carbon values takes into account the carbon emissions from the transportation and material manufacturing processes.

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

Advanced proactive anomaly detection in multi-pattern home appliances for energy optimization

As far as home appliances are concerned, a major issue arises as malfunctions frequently go unnoticed by homeowners, consuming a significant amount of energy. Identifying this problem highlights the need for an error detection technique, which is vital to minimize energy waste while enhancing household appliance efficiency. This paper introduces an innovative method for advanced proactive anomaly detection in multi-pattern home appliances using variational autoencoders (VAE). Specifically, a CNN-LSTM based VAE integrating a novel dynamic threshold method is exploited for identifying abnormal consumption usage. The overall suggested framework is a complete integrated approach comprising a training and testing phase. Both phases are initiated from robust feature engineering to enhance the multipattern operation program classification. Additionally, a smoothing and decomposition process is applied to optimize the performance of the CNN-LSTM VAE. The efficacy of the method is evaluated on data from 26 different front-load washing machines, demonstrating its effectiveness in identifying anomaly points across various washing programs. Furthermore, compared to the same approach with a static threshold, the proposed method showed improvements of up to 11. 4% on the F1 score. Finally, simulated use case scenarios indicate a reduction of nearly 30% of energy consumption, due to error prevention. As a result, the suggested approach is a robust and applicable tool for energy and demand side management.

Distributionally robust decarbonizing scheduling considering data-driven ambiguity sets for multi-temporal multi-energy microgrid operation

As concerns about environmental sustainability continue to grow, the demand for effective low-carbon energy management becomes increasingly pressing. This study presents a novel framework for multi-temporal multi-energy microgrids (MMGs), integrating advanced low-carbon technologies to meet this imperative. The framework ensures flexible operations to navigate uncertainties stemming from renewable energy sources (RES) and fluctuating energy demand. Facilitating multi-energy transactions, encompassing gas and power exchanges in both markets, the model accommodates uncertainties from RES and demand fluctuations. Objectives include reducing carbon emissions and improving economic efficiency. To address uncertainties in the MMG system, a data-driven distributionally robust optimization (DRO) method is employed. Day-ahead scheduling utilizes a two-stage three-level approach, deploying the column-and-constraints generation (C&CG) algorithm, showcasing the efficiency of DRO in minimizing energy waste and carbon emissions while remaining cost-effective. Practicality is demonstrated through real-time intra-day scheduling using the model predictive control (MPC) algorithm, building upon hourly day-ahead results. The effectiveness of both strategies is evaluated using empirical data from an MMG based on the IEEE 33-bus test system. This cost-saving framework not only achieves a significant carbon reduction of 10.6 % but also provides reliable and adaptable solutions, effectively addressing real-world variations in renewable energy and mitigating potential risks.

An Energy Management Strategy for an Electrified Railway Smart Microgrid System Based on Integrated Empirical Mode Decomposition

The integration of a renewable energy and hybrid energy storage system (HESS) into electrified railways to build an electric railway smart microgrid system (ERSMS) is beneficial for reducing fossil fuel consumption and minimizing energy waste. However, the fluctuations of renewable energy generation and traction load challenge the effectiveness of the energy management for such a complex system. In this work, an energy management strategy is proposed which firstly decomposes the renewable energy into low-frequency and high-frequency components by an integrated empirical mode decomposition (IEMD). Then, a two-stage energy distribution approach is utilized to appropriately distribute the energy flow in the ERSMS. Finally, the feasibility and effectiveness of the proposed solution are validated through case study.

Technical and Economic Challenges and Future Prospects of a Smart Grid - A Case Study

In order to improve grid efficiency, dependability, and sustainability, smart grid technology is being developed and implemented at the vanguard of updating the world's electrical infrastructure. In addition to looking at the potential implications of smart grid technology, this case study analyzes the technical and financial difficulties encountered during the implementation of a smart grid. Technical issues with energy management and system stability arise when intermittent renewable energy sources are integrated into the smart grid. To make smooth transmission, sophisticated algorithms and grid management strategies are needed. Maintaining data security and privacy is crucial since smart grids mostly depend on digital technology and data exchange. It is a constant worry to defend the grid from hackers and illegal access. The installation of sensors, smart meters, and communication equipment might come with a significant initial cost when implementing a smart grid. One of the main economic challenges is financing these developments while maintaining reasonable power bills. It may be difficult to strike a balance between the interests of technology suppliers, customers, and utilities. Real-time monitoring and control are made possible by smart grids, which improve energy distribution and minimize energy waste. In addition to lowering greenhouse gas emissions, this improves overall energy efficiency. Smart grids can enable EV charging infrastructure as electric vehicle (EV) use increases, providing potential for grid optimization and new income streams for utilities. Reliability could be increased and downtime is decreased in a smart grid when defects can be promptly identified and isolated. The implementation of a smart grid is not without its technical and financial difficulties, but the future seems bright. Overcoming these obstacles may result in an electrical grid that is more sustainable, robust, and profitable for customers as well as utilities. Stakeholder cooperation, flexible regulations, and continuous technical development are needed to address these problems. By providing practical applications and real-world insights into the implementation of a smart grid, the case study acts as a link between theoretical concepts. Stakeholders obtain a comprehensive comprehension of the intricacies involved by analyzing technical and economic obstacles. To maximize the potential of smart grid technology, this knowledge is essential for fine-tuning strategies and creating focused solutions.

Building heating by solar parabolic through collector with metallic fined PCM for net zero energy/emission buildings

Net Zero Energy/Emission Buildings is one of the most popular study goals to make systems that are very good for society. In parabolic through collector (PTC) devices, the absorber is a key part of the solar collector that takes in the concentrated sun rays from the mirrors. PTC systems are used to make steam, which is used to heat homes, run restaurants, and do work in factories, among other things. In this work, the PTC absorber was used as a test subject. Phase Change Material (PCM) was used to store heat energy so that it could be used later to heat buildings. This study concentrates on developing a more efficient and effective PTC absorber configuration that can play a pivotal role in achieving Net Zero Energy/Emission Buildings by optimizing the use of solar energy for heating purposes while minimizing energy waste and environmental impact. Hence this investigation aims to maximize the energy efficiency and minimize the exergy efficiency by optimizing the number of Fins and their positions in the PCM-embedded PTC for building heating. In this regard, this investigation considered a different number of fins (2, 4, 8, and 12) and they were placed at angles of 180°, 90°, 45°, and 30° to recover the heat back from the PCM to heat transfer fluid in the absorber. With 8 fins on the absorber, the highest average exit temperature of the heat transfer fluid (HTF) is about 94°. The PTC system's highest average energy and exergy were found to be about 72.3 % and 7.05 %, respectively, at the absorber with 12 fins. The PCM moves heat into the air for building heating uses. This keeps the building warm during the winter. Because of this, the absorber with 12 fins works better than absorbers with less fins. This type of absorber, which is made up of 12 fins, does a good job of meeting the standards of Net Zero Energy/Emission Buildings for heating buildings. When the black material was put on the top of the absorber, it cut down on the amount of heat that was lost to the surroundings.

Calibrating subjective data biases and model predictive uncertainties in machine learning-based thermal perception predictions

Heating, Ventilation, and Air Conditioning (HVAC) systems in large-scale buildings often struggle to ensure satisfactory thermal comfort for diverse occupants while minimizing energy waste. Achieving this goal requires developing reliable prediction models that capture the changing and varied occupant thermal perceptions in different spaces. Despite their widespread use, many machine learning (ML) based prediction models suffer from subjective data biases and model predictive uncertainties, causing inaccurate estimation for occupant needs and leading to suboptimal building controls. The authors propose a data-model integration method that identifies and calibrates the inherent uncertainties of existing ML models in both data and model dimensions, ensuring reliable thermal perception predictions. This method introduces the Multidimensional Association Rule Mining (M-ARM) algorithm to identify biased human responses by exploring interrelationships among four perception metrics: thermal sensation, comfort, acceptability, and preference. Our method reveals significant performance enhancements in seven ML models, enhancing the F1-score by up to 5.53%. By leveraging reliability diagrams and Expected Calibration Error (ECE) scores, we also expose the models’ vulnerability to miscalibration and the need for calibrated predictions. We further evaluate six calibration techniques (e.g., Platt Scaling and Isotonic Calibration) on these models and uncover their potential to enhance prediction reliability performance, highlighting a reduction of up to 80.66% in ECE scores. The authors also investigated the impacts of dataset sizes, classifiers, and calibration methods on the proposed method. Our research offers insight into creating robust data-driven strategies for thermal perception predictions, ultimately contributing to optimized occupant comfort and energy efficiency in buildings.

The Impact of Digital Business on Energy Efficiency in EU Countries

Digital business plays a crucial role in driving energy efficiency and sustainability by enabling innovative solutions such as smart grid technologies, data analytics for energy optimization, and remote monitoring and control systems. Through digitalization, businesses can streamline processes, minimize energy waste, and make informed decisions that lead to more efficient resource utilization and reduced environmental impact. This paper aims at analyzing the character of digital business’ impact on energy efficiency to outline the relevant instruments to unleash EU countries’ potential for attaining sustainable development. The study applies the panel-corrected standard errors technique to check the effect of digital business on energy efficiency for the EU countries in 2011–2020. The findings show that digital business has a significant negative effect on energy intensity, implying that increased digital business leads to decreased energy intensity. Additionally, digital business practices positively contribute to reducing CO2 emissions and promoting renewable energy, although the impact on final energy consumption varies across different indicators. The findings underscore the significance of integrating digital business practices to improve energy efficiency, lower energy intensity, and advance the adoption of renewable energy sources within the EU. Policymakers and businesses should prioritize the adoption of digital technologies and e-commerce strategies to facilitate sustainable energy transitions and accomplish environmental objectives.

Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses

Accurate temperature prediction and modeling are critical for effective management of agricultural greenhouses. By optimizing control and minimizing energy waste, farmers can maintain optimal environmental conditions, leading to improved crop yields and reduced financial losses. In this study, multiple models, including Multiple Linear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM), were compared to predict greenhouse air temperature. External parameters, such as air temperature (Tout), relative humidity (Hout), wind speed (W), and solar radiation (S), were used as inputs for these models, and the output was the inside temperature. The results showed that the RBF model with the LM (Levenberg–Marquardt) learning algorithm outperformed the other models, achieving the lowest error and the highest coefficient of determination (R2) value. The RBF model produced RMSE, MAPE, and R2 values of 1.32 °C, 3.23%, and 0.931, respectively. These results demonstrate that the RBF model with the LM learning algorithm can reliably predict greenhouse air temperatures for the next two hours. The ANN model can be applied to optimize time management and reduce energy losses, improving the overall efficiency of greenhouse operations.

Energy efficiency management according to ISO 50001: A case study in the brick industry

This research presents the methodology and results of implementing energy efficiency management in the brick industry, given the problem of high electricity consumption in the production processes. Based on the ISO 50001 standard, energy efficiency management has as its structure the PHVA methodology of the Deming cycle and indicators that meet the standard's requirements. Energy consumption in tons of bricks produced is established as an indicator, allowing proposals for improving performance and efficient energy use, as well as implementing a management system, minimizing energy waste, and implementing engineering tools in the processes. Energy consumption data were collected before and after implementation, these data were analyzed, and the decrease in monthly electricity consumption was verified through a pre-test conducted at the beginning of the research, recording parameters of 543,800 kWh. After implementation, a post-test was conducted, recording parameters of 500,296 kWh, resulting in a saving of 43,504 kWh; in monetary units, the saving is S/18,067.21 for each month of production. Obtaining an annual decrease of 522,048 kWh, represented in monetary units S/216,806.53 (US$ 59,891.30 exchange rate S/3.62). Therefore, it is proven that implementing the methodology is feasible through the management of energy efficiency based on ISO 50001 and contributes strategically to the brick industry by increasing the efficiency associated with the reduction of 8% monthly electricity consumption.

Surrogate model of a HVAC system for PV self-consumption maximisation

In the last few years, energy efficiency has become a challenge. Not only mitigating environmental impact but reducing energy waste can lead to financial advantages. Buildings play an important role in this: they are among the biggest consumers. So, finding manners to reduce energy consumption is a way to minimise energy waste, and a technique for that is creating Demand Response (DR) strategies. This paper proposes a novel way to decrease computational effort of simulating the behaviour of a building using surrogate models based on active learning. Before going straight to the problem of a building, which is complex and computationally costly, the paper proposes the approach of active learning to a smaller problem: with reduced simulations, regress the curve of voltage versus current of a thermo-resistor. Then, the paper implements a surrogate model of energy consumption of a building. The goal is to be able to learn the consumption pattern based on a limited number of simulations. The result given by the surrogate can be used to set the reference temperature, maximising the PV self-consumption, and reducing energy usage from the grid. Thanks to the surrogate, the total time spent to map all possible consumption scenarios is reduced around 7 times.

Multi-stage voltage control in high photovoltaic based distributed generation penetrated distribution system considering smart inverter reactive power capability

The intermittent nature of photovoltaic (PV) based distributed generation can cause voltage control issues. This research aims to investigate the impact of using the reactive power capability of PV smart inverters, which can function as distributed static compensators (DSTATCOMs) during non-feed-in hours, to address this problem. In other words, the suggested PV-DSTATCOM can be used to provide voltage control whenever there is a high demand placed on the system around the clock. This study presents a coordinated multi-stage voltage control (CMSVC) strategy that utilizes both PV-DSTATCOMs and traditional voltage control devices through a hybrid of local and centralized control algorithms. The goal is to minimize energy waste while maintaining a voltage that is within acceptable limits. To achieve the best results, an improved whale optimization algorithm has been proposed for optimal optimization. To test the proposed method, the IEEE 33 bus radial distribution system and IEEE 69 bus radial distribution system were evaluated. According to the findings, the solution offered in this research significantly reduces energy losses and voltage variations, demonstrating the effectiveness of the proposed method