Advanced Warehouse Energy Storage System Control Using Deep Supervised and Reinforcement Learning

The world is undergoing a shift from fossil fuels to renewable energy sources due to the threat of global warming, which has led to a substantial increase in complex buildingintegrated energy systems. These systems increasingly feature local renewable energy production and energy storage systems that require intelligent control algorithms. Traditional approaches, such as rule-based algorithms, are dependent upon timeconsuming human expert design and maintenance to control the energy systems efficiently. Although machine learning has gained increasing amounts of research attention in recent years, its application to energy cost optimization in warehouses still remains in a relatively early stage. Suggested newer approaches are often too complex to implement efficiently, very computationally expensive, or lacking in performance. This Ph.D. thesis explores, designs, and verifies the use of deep learning and reinforcement learning approaches to solve the bottleneck of human expert resource dependency with respect to efficient control of complex building-integrated energy systems. A technologically advanced smart warehouse for food storage and distribution is utilized as acase study. The warehouse has a commercially available Intelligent Energy ManagementSystem (IEMS).publishedVersio

ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse

Author's accepted manuscriptIndustrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management system. The operating temperature and pressure measurements, as well as the operating frequency of compressors, are used in developing operational model of the cooling system, which outputs electrical consumption and refrigerant mass flow without the need for additional physical measurements. The presented model is superior to a generalized theoretical model, as it learns from data that includes individual compressor type characteristics. The results show that the presented approach is relatively precise with a Mean Average Percentage Error (MAPE) as low as 5%, using low resolution and asynchronous data from a case study system. The developed model is also tested in a laboratory setting, where MAPE is shown to be as low as 1.8%.acceptedVersio

Energieffektivisering og strategisk implementering i Bergen Kommune

Energieffektiviseringstiltak utgjør ifølge International Energy Agency 65 % av den nødvendige reduksjonen av CO2-utslipp frem mot 2020 for å kunne stabilisere den allerede høye konsentrasjonen i atmosfæren. Bergen Kommune var tidlig ute med å gjennomføre energieffektiviseringstiltak og har i dag energieffektivisering av egen bygningsmasse med som et eget punkt i sin strategiske planlegging og målsetting. Bergen Kommune ansatte Norges første kommunale klimasjef i 2008 og var første kommune som utarbeidet egen klimaplan i år 2000. Denne masteravhandlingen ser på hvordan mellomledelsen i Bergen Kommune jobber med implementeringen av energieffektiviseringsstrategi. Studien behandler energieffektivisering som et strategisk og ikke et isolert økonomisk spørsmål. Det er gjennomført en grundig dokumentanalyse og kvalitative intervjuer med mellomledere. Studiens hovedfunn indikerer at implementeringsarbeidet bærer preg av en fragmentert og individuell innsats. Det er spesielt knyttet utfordringer til vertikal og horisontal kommunikasjon, fragmentert budsjettering og mangel på gode kontrollsystemer. Det har vært problemer knyttet til å dokumentere faktisk besparelse ved isolerte tiltak. Bergen Kommune har et grundig strategisk planleggingsarbeid, men mangler tilsynelatende kompetanse og kunnskap om strategisk implementering