Forecasting of residential unit's heat demands: a comparison of machine learning techniques in a real-world case study

A large proportion of the energy consumed by private households is used for space heating and domestic hot water. In the context of the energy transition, the predominant aim is to reduce this consumption. In addition to implementing better energy standards in new buildings and refurbishing old buildings, intelligent energy management concepts can also contribute by operating heat generators according to demand based on an expected heat requirement. This requires forecasting models for heat demand to be as accurate and reliable as possible. In this paper, we present a case study of a newly built medium-sized living quarter in central Europe made up of 66 residential units from which we gathered consumption data for almost two years. Based on this data, we investigate the possibility of forecasting heat demand using a variety of time series models and offline and online machine learning (ML) techniques in a standard data science approach. We chose to analyze different modeling techniques as they can be used in different settings, where time series models require no additional data, offline ML needs a lot of data gathered up front, and online ML could be deployed from day one. A special focus lies on peak demand and outlier forecasting, as well as investigations into seasonal expert models. We also highlight the computational expense and explainability characteristics of the used models. We compare the used methods with naive models as well as each other, finding that time series models, as well as online ML, do not yield promising results. Accordingly, we will deploy one of the offline ML models in our real-world energy management system in the near future

Efficiency and Optimization of Buildings Energy Consumption: Volume II

This reprint, as a continuation of a previous Special Issue entitled “Efficiency and Optimization of Buildings Energy Consumption”, gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption

OPTIMUM DESIGN AND OPERATION OF COMBINED COOLING HEATING AND POWER SYSTEM WITH UNCERTAINTY

Combined cooling, heating, and power (CCHP) systems utilize renewable energy sources, waste heat energy, and thermally driven cooling technology to simultaneously provide energy in three forms. They are reliable by virtue of main grid independence and ultra-efficient because of cascade energy utilization. These merits make CCHP systems potential candidates as energy suppliers for commercial buildings. Due to the complexity of CCHP systems and environmental uncertainty, conventional design and operation strategies that depend on expertise or experience might lose effectiveness and protract the prototyping process. Automation-oriented approaches, including machine learning and optimization, can be utilized at both design and operation stages to accelerate decision-making without losing energy efficiency for CCHP systems. As the premise of design and operation for the combined system, information about building energy consumption should be determined initially. Therefore, this thesis first constructs deep learning (DL) models to forecast energy demands for a large-scale dataset. The building types and multiple energy demands are embedded in the DL model for the first time to make it versatile for prediction. The long short-term memory (LSTM) model forecasts 50.7% of the tasks with a coefficient of variation of root mean square error (CVRMSE) lower than 20%. Moreover, 60% of the tasks predicted by LSTM satisfy ASHRAE Guideline 14 with a CVRMSE under 30%. Thermal conversion systems, including power generation subsystems and waste heat recovery units, play a vital role in the overall performance of CCHP systems. Whereas a wide choice of components, nonlinear characteristics of these components challenge the automation process of system design. Therefore, this thesis second designs a configuration optimization framework consisting of thermodynamic cycle representation, evaluation, and optimizer to accelerate the system design process and maximize thermal efficiency. The framework is the first one to implement graphic knowledge and thermodynamic laws to generate new CO2 power generation (S-CO2) system configurations. The framework is then validated by optimizing the S-CO2 system's configurations under simple and complex component number limitations. The optimized S-CO2 system reaches 49.8% thermal efficiency. This efficiency is 2.3% higher than the state of the art. Third, operation strategy with uncertainty for CCHP systems is proposed in this thesis for a hospital with a floor area of 22,422 m2 at College Park, Maryland. The hospital energy demands are forecasted from the DL model. And the S-CO2 power subsystem is implemented in CCHP after optimizing from the configuration optimizer. A stochastic approximation is combined with an autoregression model to extract uncertain energy demands for the hospital. Load-following strategies, stochastic dynamic programming (SDP), and approximation approaches are implemented for CCHP system operation without and with uncertainties. As a case study, the optimization-based operation overperforms the best load-following strategy by 14% of the annual cost. Approximation-based operation strategy highly improves the computational efficiency of SDP. The daily operating cost with uncertain cooling, heating, and electricity demands is about 0.061 $/m2, and a potential annual cost is about 22.33$/m2. This thesis fills the gap in multiple energy types forecast for multiple building types via DL models, prompts the design automation of S-CO2 systems by configuration optimization, and accelerates operation optimization of a CCHP system with uncertainty by an approximation approach. In-depth data-driven methods and diversified optimization techniques should be investigated further to boost the system efficiency and advance the automation process of the CCHP system

Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.Zlepšení průmyslových procesů, Model založený na datech, Optimalizace procesu, Strojové učení, Průmyslové systémy, Energeticky náročná průmyslová odvětví, Umělá inteligence.

Multi-scale and dynamic energy mapping for strategic decision making and integrated energy management in Wallonia

In the context of smart cities, this doctoral thesis addresses the energy challenge linked to existing building stocks, by proposing methods and tools for estimating and analysing their energy consumption on the territorial scale, in combination with multi-scale and dynamic energy mapping. The methodologies and tools developed are applied to the entire stock of buildings in Wallonia (Belgium), which includes more than 1.7 million buildings. The results should help implement smart energy management in large building stocks. Firstly, the annual heat consumption (HC), heat demand (HD), and electricity consumption (EC) of the regional building stock are assessed, statistically analysed and mapped on different scales. Based on mean values at the neighbourhood scale, the HD is lower than the HC of 16.44%, 15.78% and 9.26% for the residential, tertiary industrial buildings respectively. Statistical analysis tests were performed to analyse to what extent different types of variables explain the annual EC. Moreover, the impact of climate change on the existing building stock's HC and cooling EC evolution until 2050 is performed using artificial intelligence models. The HC reduction of the entire building stock until 2050, calculated at the regional scale, reaches -8.82 % for residential, -10.00% for tertiary, and -11.26% for industrial buildings. The projected increase in EC for cooling in existing tertiary buildings is + 11.94% in 2050. Further, the land use mix (LUM) of residential, tertiary and industrial buildings on a statistical sector scale is assessed based on entropy (E) and Herfindahl-Hirschman Index (HHI). On the 12 generated LUM classes, 3 prospective scenarios based on climate change, buildings renovation rate, and demography are applied. Energy consumption reduction tendencies are different in classes. Finally, the dynamic hourly HC and EC profiles per m² of different building archetypes are modelled, using sigmoid functions and programming in Python, based on previously assessed annual HC and EC and the temperature data. The simulated dynamic hourly profiles of HC and EC of 4 building archetypes are calibrated and validated using monitoring data and indices proposed by ASHRAE.Wal-e-Citie

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems