Optimize Energy Usage Research Articles

Energy Consumption Reduction in Underground Mine Ventilation System: An Integrated Approach Using Mathematical and Machine Learning Models Toward Sustainable Mining

This study presents an integrated approach combining the Hardy Cross method and a gradient boosting (GB) optimization model to enhance ventilation systems in underground mines, with a specific application at the Jabal Sayid mine in Saudi Arabia. The Hardy Cross method addresses variations in airflow resistance caused by obstacles within ventilation pathways, enabling accurate predictions of the flow distribution across the network. The GB model complements this by optimizing fan placement, pressure control, and airflow intensity to achieve reduced energy consumption and improved efficiency. The results demonstrate significant improvements in fan efficiency, optimized energy usage, and enhanced ventilation effectiveness, achieving a 31.24% reduction in electricity consumption. This study bridges deterministic and machine learning methodologies, offering a novel framework for the real-time optimization of underground mine ventilation systems. By combining the Hardy Cross method with GB, the proposed approach outperforms traditional techniques in predicting and optimizing airflow distribution under dynamic conditions.

Remotely Operated Video Enhanced Receiver

The "Remotely Operated Video Enhanced Receiver (ROVER) is a versatile system designed for remote environmental monitoring and data acquisition, tailored for challenging terrains, including extraterrestrial surfaces such as the Moon. The project integrates multiple sensors and a real-time video system to provide precise and actionable insights. A soil moisture sensor detects the presence of water or moisture in the substrate, triggering an LED to blink as an indicator. Additionally, a smoke and gas detector identifies harmful gases or smoke in the air, with an LED notification to alert the user to environmental hazards. These features make the system highly effective for monitoring and exploration in remote or hazardous environments. The system also employs an LDR (Light Dependent Resistor) to automatically activate the solar panel, optimizing energy usage by harnessing sunlight when available. A camera module provides real-time video streaming and recording capabilities, enabling visual observation of the surroundings. Designed for remote operation, ROVER offers a practical solution for applications such as planetary exploration, environmental hazard monitoring, and autonomous research operations. The integration of sensors with automated alerts and video recording enhances its usability and reliability for various scientific and industrial purposes.

Digital Twin Smart City Visualization with MoE-Based Personal Thermal Comfort Analysis

Digital twin technology us used to create accurate virtual representations of objects or systems. Digital twins span the object’s life cycle and keep updated with real-time data. Therefore, their simulation capabilities can be combined with deep learning to create a system that simulates scenarios, enabling analysis. As cities continue to grow and the demand for sustainable development increases, digital twin technology, combined with AI-driven analysis, will play a critical role in shaping the future of urban environments. The ability to accurately simulate and manage complex systems in real time opens up new possibilities for optimizing energy usage, reducing costs, and improving the quality of life for urban residents. In this study, a digital twin application is built to visualize a smart area in South Korea, utilizing a deep learning model for personal thermal comfort analysis, which can be useful for managing and saving building and household energy consumption. Using Cesium for Unreal, a powerful tool for integrating 3D geospatial data, and leveraging DataSmith to convert 3D data into Unreal Engine format, this study also contributes a roadmap for smart city application development, which is currently considered to be lacking. By creating a robust framework for smart city applications, this research not only addresses current challenges but also lays the groundwork for future innovations in urban planning and management.

A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting

With the proliferation of smart home devices and the ever-increasing demand for household energy management, very-short-term load forecasting (VSTLF) has become imperative for energy usage optimization, cost saving and for sustaining grid stability. Despite recent advancements, VSTLF in the household scenario still poses challenges. For instance, some characteristics (e.g., high-frequency, noisy and non-stationary) exacerbate the data processing and model training procedures, and the heterogeneity in household consumption patterns causes difficulties for models with the generalization capability. Further, the real-time data processing requirement calls for both the high forecasting accuracy and improved computational efficiency. Thus, we propose a diffusion–attention-enhanced temporal (DATE-TM) model with multi-feature fusion to address the above issues. First, the DATE-TM model could integrate residents’ electricity consumption patterns with climatic factors. Then, it extracts the temporal feature using an encoder and meanwhile models the data uncertainty through a diffusion model. Finally, the decoder, enhanced with the attention mechanism, creates the precise prediction for the household load forecasting. Experimental results reveal that DATE-TM significantly surpasses classical neural networks such as BiLSTM and DeepAR, especially in handling the data uncertainty and long-term dependency.

Investigating the Impact of AI/ML for Monitoring and Optimizing Energy Usage in Smart Home

Integrating artificial intelligence (AI) and machine learning (ML) into smart home systems has significantly advanced and improved residential energy efficiency, addressing growing concerns around energy conservation and sustainability. Choosing appropriate AI/ML techniques to optimize energy consumption in the dynamic and contemporary smart home environment remains a complex challenge. This study investigates a range of AI/ML algorithms such as regression models, deep learning, clustering, and decision trees to enhance energy management in smart homes. The study highlights the core processes of smart home energy optimization, including data acquisition, feature extraction, and model evaluation, as well as the specific roles of each AI/ML technique in optimizing energy usage. The study also discusses the strengths and weaknesses of the AI/ML techniques used for smart homes. It further explores the application areas and emerging challenges such as data security risks, high implementation costs, and gaps in existing technology that impact the scalability of AI/ML solutions in smart home contexts. The findings reveal that AI/ML techniques can effectively transform energy management in smart homes, enabling real-time optimization and adaptive decision-making to minimize energy consumption and reduce costs. Additionally, the study highlights future research directions.

The design and implementation of Intelligent-Electricity Consumption and Billing Information System (IEBCIS)

The increasing demand for electricity and the need to reduce carbon emissions have made optimizing energy usage and promoting sustainability critical in the modern economy. This research paper explores the design and implementation of an Intelligent-Electricity Consumption and Billing Information System (IEBCIS), focusing on its role in addressing electricity sustainability challenges. Using the Design Science Research (DSR) methodology, the system’s architecture collects, analyses, and visualizes electricity usage data, providing users with valuable insights into their consumption patterns. The research involved developing and validating the IEBCIS prototype, with results demonstrating enhanced real-time monitoring, load shedding schedules, and billing information. These results were validated through user testing and feedback, contributing to the scientific knowledge of intelligent energy management systems. The contributions of this research include the development of a framework for intelligent energy management and the integration of data-driven insights to optimize electricity consumption, reduce costs, and promote sustainable energy use. This research was conducted over a time scope of two years (24 months) and entails design, development, pilot test implementation and validation phases.

Energy Management System for Distributed Energy Resources using Blockchain Technology

: Power generation in today’s world is of utmost importance, due to which blockchain is used for the categorization and formation of decentralized structures. This paper has proposed decentralized energy generation using a nester, i.e., energy sharing without third-party intervention. Decentralized blockchain technology is applied to ensure power sharing between buyer and seller, and also to achieve efficient power transmission between prosumer and consumer. Energy management is associated with controlling and reducing energy consumption. Blockchain technology plays a major role in distributed power generation, for example, power-sharing (solar and wind energy), price fixation, energy transaction monitoring, and peer-to-peer power-sharing. These are operations performed by blockchain in renewable power generation. Solar power generation using blockchain technology can obtain an impact resting upon the power generation system. Distributed ledger is the key area of blockchain technology for recording and tracking each transaction in the distribution system to improve the efficiency of the overall transmission system. A smart contract is another important tool in the blockchain technology, which is issued to confirm an assent between buyer and seller before starting any energy transaction without external intervention and also to avoid time delay. Maximum power point tracking is conducted in PV cells using blockchain technology. Blockchain influences energy management systems to improve the utilization of energy, optimize energy usage, and also to reduce the cost.

Towards Sustainable Campus Development: A Case Study of Klaipeda University

Klaipeda University (KU) has initiated a transformative project to create a green campus. This involves integrating renewable energy sources and smart building systems to improve resource efficiency and encourage sustainable living practices. An assessment of these initiatives examines their impact on sustainability, energy consumption, and educational outcomes. The evaluation involves a mixed-methods research approach, utilizing both qualitative and quantitative data from technical reports, system performance metrics, and academic literature. The findings reveal positive developments in energy management, with geothermal and solar energy systems significantly reducing the university's carbon footprint. The implementation of building management systems (BMS) has led to optimized energy usage, resulting in a decrease in environmental impact. Moreover, the green campus initiatives have enhanced the educational experience by promoting a culture of sustainability within the university community. This case study offers a valuable model for other regional institutions aiming to adopt similar sustainable practices and technologies, aligning with the United Nations Sustainable Development Goals (SDGs).

An Intelligent System for Light and Air Conditioner Control Using YOLOv8

High energy consumption in classrooms is a significant concern, often resulting from inefficient lighting and air conditioning systems. Specifically, the problem lies in the lack of automated control mechanisms that adjust energy use based on real-time occupancy data. This study aims to develop and evaluate a system that employs a camera integrated with the YOLOv8 algorithm to detect human presence and optimize energy usage by controlling lights and air conditioning. The system's performance was assessed in three different classroom environments: two large and one small. The system's accuracy for occupancy detection varied from 13.64% to 100%, depending on lighting conditions and room size. Light control accuracy was highest in the classrooms with consistent lighting, reaching 99.77%. Air conditioning control achieved perfect accuracy of 100% in the classroom with a SHARP brand AC, with a maximum remote-control range of 7 meters. These findings indicate that the system's performance is influenced by lighting conditions and room size, with smaller rooms showing better results. The system demonstrates promising potential for reducing energy consumption in classroom settings, thereby contributing to more sustainable energy practices.

CREATING DIGITAL TWINS OF SMART BUILDINGS ON THE AZURE DIGITAL TWINS PLATFORM

The relevance of developing digital twins of smart buildings on the Azure Digital Twins platform is shaped by numerous factors reflecting contemporary trends in the construction industry and property management. The escalating demand for resource efficiency, optimizing energy usage, and augmenting user comfort are propelling the shift towards intelligent building systems. Azure Digital Twins presents remarkable capabilities for crafting digital models of buildings and their virtual representation. This enables the integration of innovative technologies like the Internet of Things (IoT), artificial intelligence, and data analytics to enhance building management. A key contemporary advantage lies in the real-time tracking of a building's systems, encompassing energy consumption, security, ventilation, and various facets. This facilitates prompt responses to ongoing issues and proactively prevents malfunctions. In the context of escalating demands for sustainable construction and energy-efficient management, creating digital twins of smart buildings on Azure Digital Twins becomes a necessary step towards achieving high productivity, cost reduction, and fostering comfortable living and working environments. The creation of digital twins for smart buildings on the Azure Digital Twins platform holds significance in municipal infrastructure management and resource optimization. This encompasses energy management, water supply, waste management, and other aspects of municipal services. In the broader context of overall IT development, creating digital twins of smart buildings reflects a powerful driver towards societal digital transformation. Integrating IoT, data analytics, and artificial intelligence into municipal infrastructure management heightens efficiency, rendering management processes more flexible and adaptive to change. Information technologies based on digital twins of smart buildings stimulate the advancement of intelligent cities, elevating residents' comfort and quality of life. The ontological approach allows for constructing models that not only accurately reflect physical object characteristics but also ensure a structured understanding of relationships between different elements. This enables efficient data analysis, prediction of potential changes, and real-time responsiveness. The application of ontologies in constructing digital twins emerges as a pivotal stage in IoT and smart technology industry evolution. It not only aids in virtual object modeling but also unlocks doors for innovative solutions in data analytics, forecasting, and management process automation.

Does functional urban specialization affect energy efficiency? Evidence from China

In light of China's rapid urbanization and industrialization, adopting a spatial cooperation system that leverages cities’ comparative advantages is vital for efficient resource allocation. This paper investigates the impact of functional urban specialization on energy efficiency, considering the moderating role of market integration, using provincial panel data from 2000 to 2019. The findings underscore the importance of energy efficiency in China's development strategy, revealing a U-shaped relationship between functional urban specialization and energy efficiency. This indicates that only a few provinces have currently surpassed the critical threshold. The rationalization of industrial structures and technological innovation are identified as key mechanisms through which functional urban specialization enhances energy efficiency. Furthermore, enhanced market integration significantly amplifies this positive effect. Based on these insights, it is recommended that provinces promote specialized urban development and strengthen regional economic integration to optimize energy usage and facilitate sustainable growth.

Reducing Uncertainty in Energy Hub Operation: A Demand Response Approach for Improved Cost-Efficiency

Abstract In this paper, a daily programming was performed for an Energy Hub (EH). This EH has a typical electricity generation unit, a boiler, a Combined Heat and Power (CHP), two hectares of solar farm, three wind turbines with different capacities, a battery and a heat storage unit. This hub can purchase electricity from the upstream grid and is also responsible for fully supplying the electrical and thermal loads. Herein, power generation uncertainty by Renewable Energy Sources (RESs), the consumer's electrical and thermal demands as well as the upstream grid electricity market price are modeled by appropriate probability functions. In addition, the combined thermal and electrical energies to consumer are optimized. Moreover, the effect of uncertainty of uncertain parameters is reduced using a Demand Response Program (DRP) by load shifting method. The DRP is applied to both energy forms (electrical and thermal), which reduces hub costs by shifting the load from the hours when energy is expensive and unavailable to the hours when energy is cheap and available. Energy storage devices also shift energy from expensive to cheap hours. The integration of a Demand Response Program effectively minimizes operational costs and enhances the efficiency of the Energy Hub by optimizing energy usage in response to fluctuating prices and availability.

Guest Editorial: Patience in AI Development Is a Virtue: Why 99% Correct Is 100% Wrong

In heavy-asset industries such as oil and gas, precision is crucial. The saying "99% accurate is 100% wrong" reflects this reality. Despite the excitement of new technology, even minor errors can have significant consequences. For example, an artificial intelligence (AI) system inaccurately predicting a critical machinery component's lifespan could lead to unexpected failures, causing costly downtime and safety hazards. Kongsberg Digital, an early adopter of Microsoft's large language models (LLMs), has witnessed AI's transformative power firsthand. Over the past 2 years, these LLMs have driven a resurgence in AI interest. Generative AI's growth presents unprecedented opportunities for the heavy-asset industry, transforming interactions with complex systems. Implementing AI can optimize maintenance schedules, predict failures before they happen, and streamline operations. The rise of generative AI has also spotlighted the more traditional areas of analytics, classification, prediction, and physics-based simulations. This renewed interest has led the oil and gas industry to look for ways to utilize AI to enhance operational efficiency, reduce costs, and improve safety standards. However, AI inaccuracies or “hallucinations” are deal-breakers. AI-generated misinformation can mislead decision-makers, potentially resulting in disastrous outcomes. In some cases, using AI is unnecessary and adds little value. Heavy-asset industries prioritize safety and have historically been conservative in adopting new technologies. While post-ChatGPT developments are significant, the industry lags due to its zero tolerance for failure. This caution is justified; in environments where lives and substantial investments are at stake, even minor errors can be catastrophic. Moreover, precision in AI ensures not only safety and efficiency but also drives sustainability. Accurate AI predictions minimize waste and reduce energy consumption. Inaccurate decisions can lead to excessive energy use and waste, counteracting sustainability goals. AI can lower carbon emissions and reduce the environmental footprint by optimizing energy usage and ensuring regulatory compliance. Building trust in AI requires responsibility in development and implementation, ensuring security without compromise. We must be transparent about data lineage—a principle that guides our transformative journey. For AI to fully integrate into heavy-asset industries, it must be accurate but also secure and trustworthy. This transparency builds confidence among stakeholders, from engineers to executives.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Resilient and Adaptive Secure Routing Protocol for Wireless Sensor Networks Using a Grey Wolf Optimizer and Lightning Search Algorithm

The ability of Wireless Sensor Networks (WSNs) to efficiently monitor and collect data from difficult and distant places has made them a notable technical solution for a variety of applications. It is crucial to optimize energy usage and secure data integrity in order to put these technologies to reality with the Internet of Things (IoT). Maintaining a balance between energy economy, flexibility to changing network circumstances, and security is a common difficulty for contemporary routing systems. Because of this, the performance of these protocols drops and they become vulnerable. Specifically, for WSNs, this research presents HGL-ASRP, a state-of-the-art Resilient and Adaptive Secure Routing Protocol. By integrating the Quantum Assisted Grey Wolf Optimizer (GWO) with the Lightning Search Algorithm (LSA), this protocol overcomes the challenges faced by previous routing protocols in the Internet of Things (IoT) setting. Hybrid GWO takes cues from the social behavior of grey wolves and uses them to solve optimization problems very well. As a result of this hybrid GWO and hypercube sampling, grey wolves can only go to a better area to live. With hypercube sampling, the HGL-ASRP method performs better in a number of areas, including speed (90.9 kbps), end-to-end delay (88.8 ms), packet delivery ratio (91.2%), energy efficiency (92.09%), network lifetime (163.63 hours), and security (87.59%). The HGL-ASRP system ensures continuous data transmission even in demanding environments by successfully managing node failures and changing topologies

An experimental comparative study of energy saving based on occupancy-centric control in smart buildings

Buildings account for approximately one-third of global energy consumption and greenhouse gas emissions. Accurate occupancy data is critical for enabling energy-efficient control strategies and enhancing comfort in buildings. However, most current research on multi-zone occupancy-centric control (OCC) relies on simulated rather than real-world occupancy data. Additionally, the optimal operational intervals of existing OCC-based HVAC systems have not been fully explored in dynamic indoor environments. This study presents an extensive experimental study evaluating the impact of multi-zone real-world OCC systems on energy conservation and comfort in a multi-zone building. We collected real-world occupancy data using vision-based methods and developed HVAC control strategies using operational intervals of 5, 10, 15, 30, and 60 min to evaluate their effects on energy efficiency and occupant comfort. Simulations were performed using OpenStudio with EnergyPlus. The results indicate that customized operational intervals significantly improve both energy efficiency and occupant comfort. Shorter intervals can provide effective energy savings in dynamic settings, while longer intervals yield improved comfort and energy efficiency in more stable environments. This study demonstrates the effectiveness of OCC systems in optimizing energy usage and comfort and sets the stage for future developments in building management strategies. Emerging trends, such as integrating large language models into OCC, are also discussed for future exploration.

Optimizing energy efficiency in unrelated parallel machine scheduling problem through reinforcement learning

The industrial sector plays a significant role in global energy consumption and greenhouse gas emissions. To reduce this environmental impact, it's crucial to implement energy-efficient manufacturing systems that utilize sustainable materials and optimize energy usage. This can lead to benefits such as reduced carbon footprints and cost savings.In recent years, metaheuristic approaches have been focused on minimizing energy consumption within the Unrelated Parallel Machine Scheduling Problem (UPMSP). Traditional methods often overlook complex factors like release dates, due dates, and job setup times. This research introduces a novel algorithm that integrates reinforcement learning (RL) with a genetic algorithm (GA) to address this gap.The proposed RLGA algorithm, rooted in the dynamic field of evolutionary reinforcement learning, breaks down policies into smaller components to isolate essential parameters for problem-solving. Through comprehensive analysis, hyperparameters that influence optimal results are identified, facilitating automated hyperparameter selection and optimization. The expert system takes into account problem characteristics such as machine or job saturation, job overlap, and the maximum values of target variables, allowing instances to be grouped into clusters. These clusters are solved using a genetic algorithm with varying combinations of mutation and crossover hyperparameters. The most suitable approach for each cluster is determined by analyzing the results, and this configuration of hyperparameters is applied iteratively to optimize the solution search.The effectiveness of RLGA is evaluated across benchmark instances with different complexities, machine sets, jobs, and constraints. Comprehensive comparisons against existing methods highlight the superior performance and efficiency of RLGA in optimizing energy use and solution quality. Experimental results show that RLGA outperforms well-known solvers like CPO, CPLEX, OR-tools, and Gecode, making it a promising approach for optimizing energy-efficient manufacturing systems.

A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application.

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection.

Beyond Markovian queue: a heuristic optimization of a fluid queue with a delay-aware retrial strategy for unprecedented energy efficiency in 5G-based ad hoc networks

An energy crisis poses a significant challenge in wireless communication networks and looms large due to power-constrained supply and unforeseeable data traffic. As a solution, optimizing energy usage is crucial for enhancing data transmission efficiency and alleviating data overload. Additionally, addressing delays is vital for maintaining energy stability. Our model is dedicated to optimizing energy levels to significantly prolong the system's performance during data transmission. The proposed work thoroughly analyzes the impact of implementing a delay-aware mechanism using a fluid retrial queueing model to enhance energy optimization. Moreover, pioneered a heuristic approach for fluid queueing analysis to effectively reduce energy usage. Our numerical visualizations validate the analytical results, demonstrating an impressive 83.7% accuracy compared to traditional queueing methods.

Effects of electric vehicles on energy sharing for optimal sizing of solar PV and battery energy storage

Energy sharing for homes with electric vehicles (EVs) enhances sustainability by optimizing energy usage, reducing peak demand, and integrating renewable energy sources, thereby lowering costs and improving energy resilience. This study investigates the effects of EVs on optimal sizing problem of solar photovoltaic (SPV) and battery energy storage system (BESS) for grid-tied homes which participate in energy sharing schemes. In this paper, it is assumed that the energy is shared between two homes: home-1 as the prosumer which has an EV and intends to buy SPV and BESS, and house-2 which is a consumer. The optimization problem is formulated to achieve the minimum cost of electricity (COE) for home-1 and to reduce the COE for home-2 while taking consideration of the design constraints over the project lifespan. A rule-based energy management system is developed for different sets of configurations to compare the economic and operational results. The optimization is done by incorporating realistic annual data of the irradiance, temperature, load, and uncertainties of EV. The developed optimization technique is general in nature and can be used for any grid tied homes willing to share the electricity. Sensitivity analyses on costs of SPV-BESS, home energy demand, and grid export constraints are provided. Uncertainty analyses investigates the price of energy sharing and solar PV generation. The impact of various EV models with their respective battery capacity is also analyzed. The results show that the proposed energy-sharing methodology reduces the COE for prosumer and consumer by 1.2 ¢/kWh and 3.6 ¢/kWh, respectively.