Multi-Agent-Based Smart-Home Energy Management with Adaptive Reasoning

The aim of the study was to investigate the application and importance of Learning and Adaptive Management by the Ministry of Women Affairs, Community, Small and Medium Enterprises Development (MWACSMED) and beneficiaries when implementing development programs in Zaka district. The study investigated the objectives of the programs implemented by MWACSMED in Zaka district, the extent to which learning, risk management and adaptive management mechanisms are used by MWACSMED Zaka district are effective. The study also examined the challenges faced MWACSMED and beneficiaries with development programs in Zaka district. The study also aimed at developing a model for learning, risk management, adaptive management, monitoring, and evaluation for the efficient and effective implementation of development programs and the Monitoring, Evaluation, Learning, Adaptive and Risk Management model (MELARM) was developed. The researcher chose to conduct his research in this area because Zaka district is one of the districts in the country that is heavily reliant on subsistence farming, with farmers lacking adequate rain and inputs. Furthermore, there is no other industry or activity in the district that contributes to people's well-being. According to Chingarande and Matondi (2020:3), Zaka district is in a province that is reliant on activities such as livestock production, irrigated farming, wildlife conservation and smallholder agricultural production to support its population. Zimbabwe, where Zaka district is in the province of Masvingo used to be one of Southern Africa’s most vibrant, productive, and resilient countries, but the country has been facing economic and political challenges for close to two decades linked to decades of poor governance and corruption. Approximately 63% of Zimbabwean households are living in poverty and 16% in extreme poverty (USAID Country Development Cooperation Strategy 2016-2021:4). Given the above challenges in Zimbabwe, it is important to investigate whether decisionmakers are investing in long-term management approaches such as Adaptive Management. Decision-makers tend to be reluctant to invest in long-term management approaches which cover the concept of Adaptive Management and choose small-scale experiments instead. Moreover, they do not utilize opportunities to adjust and make improvements in their approach to management. Managers have often been making decisions based on funding opportunities that are short-term and political requests, rather than using the actual objectives of a program. The study used the quantitative approach in the form of a questionnaire that was designed using Google forms and shared through a link that was send to research participants through emails and WhatsApp. The sample population for the research questionnaire was 10 officials from MWACSMED and 7 beneficiaries of the programs managed by the Ministry in Zaka district. As shown by the study, the Ministry has not yet fully recognized the use of Learning and Adaptive Management to address the complexity and uncertainty of development programs. The Ministry has been managing programs through traditional monitoring and evaluation systems. The results of the study show that the Ministry was not using Adaptive Management approaches or learning thorough monitoring and evaluation systems, which may be because of socio-economic and political reasons. The introduction of adaptive programming and management calls for using information and learning to inform adjustments during implementation, which requires monitoring and evaluation systems that go beyond reporting on pre-specified indicators The study made recommendations for Learning and Adaptive Management mechanisms that are essential for development projects. The study also drafted a model known as the Monitoring, Evaluation, Learning, Adaptive and Risk Management Framework (MELARM). These recommendations are all based on the findings of the study. In general, government departments should avoid traditional thinking about program cycles, and they should merge program design, implementation, and evaluation through Learning and Adaptive Management. Government departments/agencies should also identify and document how procurement, contracting, work planning, reporting and risk management can foster adaptive programming and management to achieve intended development goals.

Long-term, flexible therapy strategies are needed for chronic diseases like cardiovascular ailments, diabetes, and chronic kidney disease and they have to deal with patient heterogeneity, changing physiological states and privacy constraints. This research suggests a framework for privacy-preserving and personalized artificial intelligence that combines Federated Learning (FL), a Res-HyperTransformerNet deep prediction model, Personalized Multi-Agent Reinforcement Learning (PMARL) and Explainable Artificial Intelligence (XAI) for adaptive chronic disease management. The framework is structured with two main aims: (i) to create an accurate and privacy-aware predictive model for chronic disease risk using diverse data sources, and (ii) to permit adaptive, individual therapy optimization through multi-agent reinforcement learning. Federated learning is utilized to conduct the training of Res-HyperTransformerNet over distributed Internet of Medical Things (IoMT) nodes without the requirement of transferring raw patient data. The embeddings that are created from patient data are then passed on to a PMARL module, where several agents optimize therapy dimensions like medications, diet, physical activity, and mental health interventions independently. To make the clinical support more understandable, the SHAP-based explainability method is used for both predictive and decision-making parts. The framework is tested using two public datasets—the CDC Chronic Disease dataset and the UCI Chronic Kidney Disease Risk Factor dataset. The performance is measured by employing classification metrics (accuracy, precision, recall, F1-score, MCC) as well as reinforcement learning metrics (reward score, convergence steps, episode return) and federated system metrics (communication overhead, convergence rounds, and training time). The experiments indicate that the proposed framework demonstrated improved performance in terms of predictive accuracy and policy convergence speed compared to the baseline deep learning and reinforcement learning models, and at the same time it is more cost-effective when it comes to communication in a federated setting. This means that the proposed method suggests the potential of being a reproducible and open AI framework for adaptive chronic disease therapy management. The combination of ResNet and Transformer blocks, i.e., Federated Res-HyperTransformerNet, achieved strong performance in both datasets, namely CDC Chronic Disease (Dataset 1) and UCI CKD Risk Factor (Dataset 2). The obtained accuracy for Dataset 1 was 98.61% and for Dataset 2, it was 97.75%, under the conducted experimental settings.

This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor–critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer’s preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing. Numerical examples under different weather conditions, weekday/weekend, and driving patterns of the EV confirm the effectiveness of the proposed approach in terms of total cost of electricity, state of energy of the ESS and EV, and consumer preference.

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

Facilitating adaptive management in a government program: A household energy efficiency case study

Quantifying the contribution of habitat dynamics relative to intrinsic population processes in regulating species persistence remains an ongoing challenge in ecological and applied conservation. Understanding these drivers and their relationship is essential for managing habitat‐dependent species, especially those that specialize in transitional habitats. Limitations in the ability of natural disturbance to mediate transitional habitat dynamics have resulted in a decline in early‐ and mid‐successional vegetation structure and prompted the need for aggressive habitat management to replace natural perturbations and increase habitat structural complexity. We describe a collaborative effort with a group of independent land managers to design an adaptive management program for restoring an imperiled ecosystem and recovering declining populations of an endemic habitat specialist. We developed a set of integrated, hierarchical models to estimate management‐mediated transition rates among vegetation classes in two dominant scrub communities and the species response (local colonization and extinction probabilities) as a function of habitat state. Models were fit using a long‐term data set of habitat and occupancy observations from 361 Florida scrub‐jay territories across two Florida counties. Occupancy model results correspond closely to previous approaches of estimating differential survival and reproductive success associated with habitat conditions, with highest colonization and lowest extinction rates estimated for those habitat states found to have the highest rates of survival and reproduction. In addition to offering an innovative approach for jointly modeling habitat and species population dynamics, the program we describe will also be of interest from a management perspective by providing guidance for developing collaborative, adaptive management frameworks from the ground up. We engaged land managers via workshops to specify objectives and desired state‐variable conditions, identify management alternatives, and elicit consensus opinions on model parameters. Treating expert opinions as pseudo‐observations to define Dirichlet priors allowed us to make use of existing management knowledge. Formal learning was then accumulated by updating transition probability estimates as management activities were implemented over the study period. We believe this adaptive management framework provides a useful approach for increasing our understanding of complex ecological relationships and hope that it will be adopted by others who have interest in informing management and conservation efforts.

On the basis of ecosystem adaptive management theory, this paper attempts to put forward the fundamental concept and theoretical framework of urban adaptive management (UAM), to expound the crucial essentials of UAM, and to establish the management mode and framework of UAM. Then, take Lanzhou, Gansu province of China as a study area, based on the urban metabolism theory, analysis the material metabolism of Lanzhou from 1990 to 2004 using emergy analysis approach, Meanwhile it identifies the resilience of urban metabolism process, and study its adaptive cycle process, consequently, we put forward the objects and mode of UAM of Lanzhou. The outcome of the study shows that the mode we proposed is a good way for urban management.

Marine hydrokinetic (MHK) energy offers a promising new source of renewable ocean energy. However, the young industry is faced with significant challenges. Most notable is the challenge of regulatory uncertainty that is thought to hamper the successful deployment of new tidal energy technologies. Adaptive management may be one approach to deal with uncertainty and inform permitting decisions for hydrokinetic projects. In this study, we apply the concept of adaptive management to the Cobscook Bay Tidal Energy Project in Maine to better understand and inform permitting decisions. Using a social science approach of observation, interviews, and document analysis, we examine (1) agency roles and authority, (2) agency interactions, (3) regulatory change, and (4) challenges faced in the regulatory and permitting process for MHK development at the federal and state level. We found four institutional factors favorable to an adaptive approach. These include experimentation and learning, institutionalized choice to correct avoidable error, a strong commitment to interagency coordination, and an emphasis on early proactive engagement with project developers. We also identified institutional challenges or vulnerabilities. These include conflicting agency cultures, high financial costs, and long timeframes associated with baseline data collection. Lessons learned from this study can assist regulators, policymakers, and project developers design and implement an actively adaptive management approach that can move new renewable ocean energy development forward in a way that is socially acceptable and environmentally responsible.

Adaptive management and environmental decision making: A case study application to water use planning

Purpose of ReviewTheoretical frameworks for adaptive natural resource management are quite common, whereas documented examples showing successful implementation of adaptive management and learning through multiple time intervals have remained uncommon. Measures of quality of adaptive natural resource management processes are needed to examine potential factors driving the successful implementation. To address this gap, we developed a multimetric index composed of 22 metrics to assess quality of case studies using quantitative decision support (QDS) to inform adaptive forest management (AFM). Metrics represented three main tasks, including conceptual setup, modeling, and application. We further distinguished these into subtasks: definition of objectives and management options (setup); specifying uncertainty, prediction, and optimization (modeling); and stakeholder involvement along with practice and learning (application). We used a multimetric index to examine temporal and geographic variation in quality of reviewed case studies using QDS to inform AFM. We then conducted a structured literature review of 179 articles, wherein 34 case studies met a priori criteria.Recent FindingsWhen applying the multimetric index to these case studies, we found that over the past decade the index has been intermediate and annual average scores declined by 33% from 4.5 to 3.0 of 10 (where 10 is the highest possible quality score). Aligning with reviews of adaptive natural resource management, reported on-ground application of QDS to inform AFM was rare (n = 2). We also confirmed the expectation that there has been a substantial lack of stakeholder engagement during QDS development tasks.SummaryOur multimetric index provides a novel tool to examine gaps in the use of QDS for adaptive management in diverse domains including but not limited to forests.

As more and more data can be generated at a faster-than-ever rate nowadays, it becomes a challenge to processing large volumes of data for complex data analysis. In order to address performance and cost issues of big data processing on clouds, we present a novel design of adaptive workflow management system which includes a data mining based prediction model, workflow scheduler, and iteration controls to optimize the data processing via iterative workflow tasks. We proposed a new heuristic algorithm, called Upgrade Fit, which dynamically and continuously reallocates multiple types of cloud resources to fulfill the performance and cost requirements. The iterative workflow tasks can be bursty bags of tasks to be executed repetitively for data processing. A real application of weather forecast workflow has been used to evaluate the capability of our system for large volume image data processing. Experimental system has been set up and the results indicate that the system can effectively handle multiple types of cloud resources and optimize the performance iteratively.

Current Adaptive Case Management (ACM) solutions are strong in flexibility, but business users must still meet compliance rules stemming from sources such as laws (e.g., Sarbanes-Oxley Act), standards (e.g., ISO 45001) and best practices (e.g., ITIL). This paper presents a framework on how to enable support for compliance in the context of ACM by constraints. Since ACM applications undergo constant change, there must be ways to introduce compliance rules on the fly. Currently, constraints (and similar alternative solutions) are predominately maintained by technical users, which results in long maintenance cycles. Our framework aims at enabling faster adoption of changing compliance requirements, both explicitly by enabling non-technical users (knowledge workers) to define and adapt constraints, and implicitly by learning from the decisions taken by other knowledge workers during case enactments. The former is achieved by supporting domain knowledge, maintained in an ontology. The latter is supported by a recommendation approach that enables an automated knowledge transfer between knowledge workers by propagating tacit knowledge, best practices, and the handling of constraints and their violations.

In P2P Grid computing systems, the authorization decision is often tackled by two different trust management methods: policy-based approach, where authorization are built on logical rules and verifiable properties encoded in signed credentials, and reputation-based approach, based on collecting, aggregating and disseminating reputation among the peers. However, the overhead caused by proof of compliance on authorization and the absence of certifying authorities may negate the strong and objective security advantages of policy-based approach, whilst vagueness, complexity and inaccurate characterization caused by reputation evolution may eliminate the quantitative and flexible advantages of reputation-based approach. We propose an adaptive trust management framework, which combines the merit of policy proof and reputation evolution such that authorization is aware of not only the strong and objective security traits, but also the calculability and the availability security traits. Finally, the framework of system is proposed.

This paper introduces an implementation example of an adaptive power management framework applicable to a digital TV set-top box which has energy-saving capability. The energy-saving set-top box supports a deep-sleep passive standby state in which almost all hardware functional modules are turned off except those required for a fast wake-up. Eventually, in the deep-sleep passive standby state, the power consumption level of the set-top box goes down to less than 1 watt. However, if the set-top box stays in the deep-sleep passive standby state, it can miss out the updated version of applications and EPG information. But, such shortcomings can be overcome using a mechanism for controlling power-management state transition based on set-top box usage patterns, which was proposed in the preliminary work related to this paper. This paper proposes two different methods of implementing the adaptive power management framework for the energy-saving set-top box. And this paper also shows some demonstration results.

The methods and approaches selected in a restoration project depend on the degree of ecosystem degradation and on future land use plans. Programs of monitoring and evaluation must therefore compare restoration progress and effectiveness relative to untreated conditions and to target conditions, particularly with respect to the attributes and functions essential to land use objectives. It is usually not feasible to directly monitor all important forest attributes and functions, so a few indicators must be selected. It is argued that natural recovery (or passive restoration) should always be considered a management option, and that the most appropriate evaluation of restoration techniques is by comparison with untreated areas that have been left for the same period of time as treated areas. Unambiguous evaluation therefore requires all restoration projects to be considered as formal experiments with proper controls, replicates, and the randomized application of treatment options. Adaptation to anticipated levels of climate change can be incorporated by emulating forest compositions and structures currently prevailing elsewhere under analogues of future climates and associated disturbance regimes. A structured program of monitoring and evaluation within a framework of adaptive management will engender management flexibility and resilience in an uncertain future.