Open Energy Services -- Forecasting and Optimization as a Service for Energy Management Applications at Scale

• URL: http://arxiv.org/abs/2402.15230v1
• Date: Fri, 23 Feb 2024 09:46:49 GMT
• Title: Open Energy Services -- Forecasting and Optimization as a Service for Energy Management Applications at Scale
• Authors: David W\"olfle, Kevin F\"orderer, Tobias Riedel, Lukas Landwich, Ralf Mikut, Veit Hagenmeyer, Hartmut Schmeck
• Abstract summary: We promote an approach to split the complex optimization algorithms employed by energy management systems into standardized components. This work is centered around the systematic design of a framework supporting the efficient implementation and operation of such forecasting and optimization services. It describes the implementation of the design concept which we release under the name emphEnergy Service Generics as a free and open source repository.
• Score: 0.6495316960934344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Energy management, in sense of computing optimized operation schedules for devices, will likely play a vital role in future carbon neutral energy systems, as it allows unlocking energy efficiency and flexibility potentials. However, energy management systems need to be applied at large scales to realize the desired effect, which clearly requires minimization of costs for setup and operation of the individual applications. In order to push the latter forward, we promote an approach to split the complex optimization algorithms employed by energy management systems into standardized components, which can be provided as a service with marginal costs at scale. This work is centered around the systematic design of a framework supporting the efficient implementation and operation of such forecasting and optimization services. Furthermore, it describes the implementation of the design concept which we release under the name \emph{Energy Service Generics} as a free and open source repository. Finally, this paper marks the starting point of the \emph{Open Energy Services} community, our effort to continuously push the development and operation of services for energy management applications at scale, for which we invite researchers and practitioners to participate.

Related papers

• Multi-Agent Architecture in Distributed Environment Control Systems: vision, challenges, and opportunities [50.38638300332429]
  We propose a multi-agent architecture for distributed control of air-cooled chiller systems in data centers. Our vision employs autonomous agents to monitor and regulate local operational parameters and optimize system-wide efficiency.
  arXiv  Detail & Related papers  (2025-02-21T18:41:03Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Deploying Foundation Model Powered Agent Services: A Survey [33.27330704880908]
  Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications. This paper proposes a unified framework aimed at providing a comprehensive survey on deploying FM-based agent services across heterogeneous devices.
  arXiv  Detail & Related papers  (2024-12-18T02:15:31Z)
• Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
  quantifying the energy footprint of a software system is one of the most basic activities. This document aims to be a starting point for researchers who want to begin conducting work in this area.
  arXiv  Detail & Related papers  (2024-07-16T11:21:30Z)
• Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems [0.0]
  This study introduces a novel reinforcement learning framework tailored for the co-optimisation of design and control in energy systems. By leveraging RL's model-free capabilities, the framework eliminates the need for explicit system modelling. This contribution paves the way for advanced RL applications in energy management, leading to more efficient and effective use of renewable energy sources.
  arXiv  Detail & Related papers  (2024-06-28T11:01:02Z)
• Carbon-aware Software Services [3.105112058253643]
  This article proposes a novel framework for implementing, configuring and assessing carbon-aware interactive software services. We propose a methodology to implement carbon-aware services leveraging the Strategy design pattern to feature alternative service versions with different energy consumption. We devise a bilevel optimisation scheme to configure which version to use at different times of the day, based on forecasts of carbon intensity and service requests.
  arXiv  Detail & Related papers  (2024-05-21T08:26:38Z)
• On-Demand Mobility Services for Infrastructure and Community Resilience: A Review toward Synergistic Disaster Response Systems [0.0]
  Mobility-on-demand (MOD) services have the potential to significantly improve the adaptiveness and recovery of urban systems, in the wake of disruptive events. This paper presents a review that suggests a noticeable increase within recent years on this topic across four main areas: resilient MOD services, novel usage of MOD services for improving infrastructure and community resilience, empirical impact evaluation, and enabling and augmenting technologies.
  arXiv  Detail & Related papers  (2024-03-05T16:51:02Z)
• Heuristics and Metaheuristics for Dynamic Management of Computing and Cooling Energy in Cloud Data Centers [0.0]
  We propose novel power and thermal-aware strategies and models to provide joint cooling and computing optimizations. Our results show that the combined awareness from both metaheuristic and best fit decreasing algorithms allow us to describe the global energy into faster and lighter optimization strategies. This approach allows us to improve the energy efficiency of the data center, considering both computing and cooling infrastructures, in up to a 21.74% while maintaining quality of service.
  arXiv  Detail & Related papers  (2023-12-17T09:40:36Z)
• Interpretable Deep Reinforcement Learning for Optimizing Heterogeneous Energy Storage Systems [11.03157076666012]
  Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. To enhance ESS flexibility within the energy market, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios.
  arXiv  Detail & Related papers  (2023-10-20T02:26:17Z)
• Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in Northern Sweden [0.0]
  We present an open digital ecosystem based on web-framework with a functional back-end server in user-centric energy retrofits. Data-driven web framework is proposed for building energy renovation benchmarking.
  arXiv  Detail & Related papers  (2023-09-21T08:05:10Z)
• Energy Loss Prediction in IoT Energy Services [0.43012765978447565]
  We propose a novel Energy Loss Prediction framework that estimates the energy loss in sharing crowdsourced energy services. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2023-05-16T09:07:08Z)
• Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach [48.18355658448509]
  Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy. We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
  arXiv  Detail & Related papers  (2023-04-17T02:12:30Z)
• Predict+Optimize Problem in Renewable Energy Scheduling [31.032838966665828]
  This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+ for Renewable Energy Scheduling. The top-ranked method employed optimization using LightGBM ensembles achieved at least a 2% reduction in energy costs. The novelty of this work lies in its comprehensive evaluation of Predict+ methodologies applied to a real-world renewable energy scheduling problem.
  arXiv  Detail & Related papers  (2022-12-21T02:34:12Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Movement Penalized Bayesian Optimization with Application to Wind Energy Systems [84.7485307269572]
  Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information. In this setting, the learner receives context (e.g., weather conditions) at each round, and has to choose an action (e.g., turbine parameters) Standard algorithms assume no cost for switching their decisions at every round, but in many practical applications, there is a cost associated with such changes, which should be minimized.
  arXiv  Detail & Related papers  (2022-10-14T20:19:32Z)
• Developing an AI-enabled IIoT platform -- Lessons learned from early use case validation [47.37985501848305]
  We introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection. This is complemented by insights and lessons learned during this early evaluation activity.
  arXiv  Detail & Related papers  (2022-07-10T18:51:12Z)
• Learning Implicit Priors for Motion Optimization [105.11889448885226]
  Energy-based Models (EBM) represent expressive probability density distributions. We present a set of required modeling and algorithmic choices to adapt EBMs into motion optimization.
  arXiv  Detail & Related papers  (2022-04-11T19:14:54Z)
• Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum [55.6313942302582]
  We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.
  arXiv  Detail & Related papers  (2021-08-04T07:35:14Z)
• ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy [0.8774604259603302]
  We present a runtime-based energy-allocation framework to optimize the utility of the target device under energy constraints. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users.
  arXiv  Detail & Related papers  (2021-02-26T17:21:25Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.