Model based control optimisation of renewable energy based HVAC Systems.

During the last 10 years solar cooling systems attracted more and more interest not only in the research area but also on a private and commercial level. Several demonstration plants have been installed in different European countries and first companies started to commercialise also small scale absorption cooling machines. However, not all of the installed systems operate efficiently and some are, from the primary energy point of view, even worse than conventional systems with a compression chiller. The main reason for this is a poor system design combined with suboptimal control. Often several non optimised components, each separately controlled, are put together to form a ‘cooling system’. To overcome these drawbacks several attempts are made within IEA task 38 (International Energy Agency Solar Heating and Cooling Programme) to improve the system design through optimised design guidelines which are supported by simulation based design tools. Furthermore, guidelines for an optimised control of different systems are developed. In parallel several companies like the SolarNext AG in Rimsting, Germany started the development of solar cooling kits with optimised components and optimised system controllers. To support this process the following contributions are made within the present work: - For the design and dimensioning of solar driven absorption cooling systems a detailed and structured simulation based analysis highlights the main influencing factors on the required solar system size to reach a defined solar fraction on the overall heating energy demand of the chiller. These results offer useful guidelines for an energy and cost efficient system design. - Detailed system simulations of an installed solar cooling system focus on the influence of the system configuration, control strategy and system component control on the overall primary energy efficiency. From the results found a detailed set of clear recommendations for highly energy efficient system configurations and control of solar driven absorption cooling systems is provided. - For optimised control of open desiccant evaporative cooling systems (DEC) an innovative model based system controller is developed and presented. This controller consists of an electricity optimised sequence controller which is assisted by a primary energy optimisation tool. The optimisation tool is based on simplified simulation models and is intended to be operated as an online tool which evaluates continuously the optimum operation mode of the DEC system to ensure high primary energy efficiency of the system. Tests of the controller in the simulation environment showed that compared to a system with energy optimised standard control the innovative model based system controller can further improve the primary energy efficiency by 19 %

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

Energy and Economic Performance of Solar Cooling Systems World Wide

AbstractSolar thermal cooling systems have been installed as pilot projects in most regions of the world, but due to the low number of total installations there is not yet much experience available about system sizing and design. To counter the lack of experience and to evaluate the potential of energy efficient solar cooling systems, a systematic system design study has been carried out covering most climatic regions worldwide. For each technology investigated, an energy optimized control strategy was developed which maximizes the primary energy efficiency. This control strategy was implemented in the simulation environment INSEL and system models were developed for a range of thermal cooling technologies and validated with operating experiences from different plants monitored by the authors.It could be shown that a reduction of nominal chiller power by 30% to 40% or more hardly effects the solar cooling fraction for most climates, but significantly increases the machine operating hours and thus improves the economics. The lower the nominal power of the chiller, the higher the recommended ratio of collector surface area per kW. For a given machine nominal power, solar cooling fractions increase with collector surface area until saturation is reached. Collector surface areas can be as high as 5 m2 to 10 m2 per kW with still increasing solar cooling fractions, but acceptable specific collector yield reduction. The economic optimum is reached for less solar cooling fraction and thus lower primary energy savings. Single effect absorption cooling systems easily reach 80% solar cooling fraction for all but very humid climates. Primary energy ratios can be over 3.0, depending on system design and cooling load data. CO2 and primary energy savings of 30 – 79% are achievable.The economic study showed that solar thermal cooling is more viable in hot climates than in moderate European climates. Annual costs strongly depend on the locations. The specific costs per kWh cooling in German locations vary between 0.25 and 1.01 €/kWh, in Spanish locations between 0.13 and 0.30 €/kWh. In hot climates like Jakarta and Riyadh the specific costs are as low as 0.09 to 0.15 €/kWh. Furthermore the maximum investment costs were calculated get a payback time of 10 years