| 1. |
- Andersen, Torben
(författare)
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Comparison of optimizers for model predictive thermal control of buildings
- 2024
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Ingår i: Energy and AI. - 2666-5468. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Considering recent developments in the energy sector, further reduction of electricity cost and flattening of the electric power demand curve are needed. We have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort requirements. Examples are winter heating of warehouses and vacation homes, and heat drying of buildings under construction. We have set up a system that typically reduces electricity cost by about 40% on the basis of automatic weather and real time pricing forecasts. The system uses the building as an energy reservoir over periods with high electricity cost. Using a model predictive control system, we compare use of a genetic algorithm, a particle swarm optimization, and a neural network for heater control, all working in a closed loop to reduce the influence of modeling errors. We have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance, varying only a few percent in efficiency. However, the computational and memory requirements of the neural network are much lower than for the other optimizers, so it is preferable for use with inexpensive microcontrollers. We carried out a full-scale experiment at a residential house and found agreement with simulation results.
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| 2. |
- Boriratrit, Sarunyoo, et al.
(författare)
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Adaptive Meta-Learning Extreme Learning Machine with Golden Eagle Optimization and Logistic Map for Forecasting the Incomplete Data of Solar Irradiance
- 2023
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Ingår i: Energy and AI. - : Elsevier BV. - 2666-5468. ; , s. 100243-100243
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Tidskriftsartikel (refereegranskat)abstract
- Solar energy has become crucial in producing electrical energy because it is inexhaustible and sustainable. However, its uncertain generation causes problems in power system operation. Therefore, solar irradiance forecasting is significant for suitable controlling power system operation, organizing the transmission expansion planning, and dispatching power system generation. Nonetheless, the forecasting performance can be decreased due to the unfitted prediction model and lacked preprocessing. To deal with mentioned issues, this paper proposes Meta-Learning Extreme Learning Machine optimized with Golden Eagle Optimization and Logistic Map (MGEL-ELM) and the Same Datetime Interval Averaged Imputation algorithm (SAME) for improving the forecasting performance of incomplete solar irradiance time series datasets. Thus, the proposed method is not only imputing incomplete forecasting data but also achieving forecasting accuracy. The experimental result of forecasting solar irradiance dataset in Thailand indicates that the proposed method can achieve the highest coefficient of determination value up to 0.9307 compared to state-of-the-art models. Furthermore, the proposed method consumes less forecasting time than the deep learning model.
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| 3. |
- Hagmar, Hannes, 1990, et al.
(författare)
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Real-time security margin control using deep reinforcement learning
- 2023
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Ingår i: Energy and AI. - : Elsevier BV. - 2666-5468. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- This paper develops a real-time control method based on deep reinforcement learning aimed to determine the optimal control actions to maintain a sufficient secure operating limit. The secure operating limit refers to the limit to the most stressed pre-contingency operating point of an electric power system that can withstand a set of credible contingencies without violating stability criteria. The developed deep reinforcement learning method uses a hybrid control scheme that is capable of simultaneously adjusting both discrete and continuous action variables. The performance is evaluated on a modified version of the Nordic32 test system. The results show that the developed deep reinforcement learning method quickly learns an effective control policy to ensure a sufficient secure operating limit for a range of different system scenarios. The performance is also compared to a control based on a rule-based look-up table and a deep reinforcement learning control adapted for discrete action spaces. The hybrid deep reinforcement learning control managed to achieve significantly better on all of the defined test sets, indicating that the possibility of adjusting both discrete and continuous action variables resulted in a more flexible and efficient control policy.
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| 4. |
- Han, Lei, et al.
(författare)
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Deep Neural Network-Based Generation of Planar CH Distribution through Flame Chemiluminescence in Premixed Turbulent Flame
- 2023
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Ingår i: Energy and AI. - : Elsevier BV. - 2666-5468. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Flame front structure is one of the most fundamental characteristics and, hence, vital for understanding combustion processes. Measuring flame front structure in turbulent flames usually needs laser-based diagnostic techniques, mostly planar laser-induced fluorescence (PLIF). The equipment of PLIF, burdened with lasers, is often too sophisticated to be configured in harsh environments. Here, to shed the burden, we propose a deep neural network-based method to generate the structures of flame fronts using line-of-sight CH* chemiluminescence that can be obtained without the use of lasers. A conditional generative adversarial network (C-GAN) was trained by simultaneously recording CH-PLIF and chemiluminescence images of turbulent premixed methane/air flames. Two distinct generators of the C-GAN, namely Resnet and U-net, were evaluated. The former net performs better in this study in terms of both generating snap-shot images and statistics over multiple images. For chemiluminescence imaging, the selection of the camera's gate width produces a trade-off between the signal-to-noise (SNR) ratio and the temporal resolution. The trained C-GAN model can generate CH-PLIF images from the chemiluminescence images with an accuracy of over 91% at a Reynolds number of 5000, and the flame surface density at a higher Reynolds number of 10,000 can also be effectively estimated by the model. This new method has the potential to achieve the flame characteristics without the use of laser and significantly simplify the diagnosing system, also with the potential for high-speed flame diagnostics.
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| 5. |
- Lindahl, Johan, et al.
(författare)
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Mapping of decentralised photovoltaic and solar thermal systems by remote sensing aerial imagery and deep machine learning for statistic generation
- 2023
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Ingår i: ENERGY AND AI. - : Elsevier BV. - 2666-5468. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- As a mean to monitor the rapid expansion of the highly decentralized PV market, identifying solar energy systems in aerial imagery by deep machine learning, is a research field that is getting increasing interest. One general challenge in the field is to create testing data of high quality that are representative of the end-use application. In this study we use the open source convolutional neural network developed within the DeepSolar project and apply it in the country of Sweden, for the purpose of generating market statistics, by scanning three complete municipalities for small decentralized photovoltaic and solar thermal systems. The evaluation of the performance is done against a highly accurate ground truth, which was created by cross-checking the classification results with the inventory of the local distribution system operators and the database of photovoltaic systems that have received a capital subsidy in Sweden, and combining that with physical onsite inspections. A process of generate additional training data and re-training the algorithm after each municipality scan was developed, which successively improved the accuracy, resulting in that 95% of all detectable photovoltaic, excluding building integrated and vertical systems, and 80% of all detectable solar thermal systems were correctly identified in the last municipality scan. The accurate ground truth allowed a quantification of why some systems are not detected. The generated dataset of solar energy systems could be connected to existing building and property inventories, which allowed creation of market segment statistics with remarkably high detail information.
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| 6. |
- Machalek, Derek, et al.
(författare)
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Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models
- 2022
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Ingår i: Energy and AI. - : Elsevier BV. - 2666-5468. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.
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| 7. |
- Olsson, Tomas, et al.
(författare)
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A data-driven approach for predicting long-term degradation of a fleet of micro gas turbines
- 2021
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Ingår i: Energy and AI. - : Elsevier BV. - 2666-5468. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Predictive health monitoring of micro gas turbines can significantly increase the availability and reduce the operating and maintenance costs. Methods for predictive health monitoring are typically developed for large-scale gas turbines and have often focused on single systems. In an effort to enable fleet-level health monitoring of micro gas turbines, this work presents a novel data-driven approach for predicting system degradation over time. The approach utilises operational data from real installations and is not dependent on data from a reference system. The problem was solved in two steps by: 1) estimating the degradation from time-dependent variables and 2) forecasting into the future using only running hours. Linear regression technique is employed both for the estimation and forecasting of degradation. The method was evaluated on five different systems and it is shown that the result is consistent (r>0.8) with an existing method that computes corrected values based on data from a reference system, and the forecasting had a similar performance as the estimation model using only running hours as an input.
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| 8. |
- van Zoest, Vera, 1992-, et al.
(författare)
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Evaluating the effects of the COVID-19 pandemic on electricity consumption patterns in the residential, public, commercial and industrial sectors in Sweden
- 2023
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Ingår i: Energy and AI. - : Elsevier. - 2666-5468. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic has had drastic effects on societies around the world. Due to restrictions or recommendations, companies, industries and residents experienced changes in their routines and many people shifted to working from home. This led to alterations in electricity consumption between sectors and changes in daily patterns. Understanding how various properties and features of load patterns in the electricity network were affected is important for forecasting the network's ability to respond to sudden changes and shocks, and helping system operators improve network management and operation. In this study, we quantify the extent to which the COVID-19 pandemic has led to shifts in the electricity consumption patterns of different sectors in Sweden. The results show that working from home during the pandemic has led to an increase in the residential sector's total consumption and changes in its consumption patterns, whereas there were only slight decreases in the industrial sector and relatively few changes in the public and commercial sectors. We discuss the reasons for these changes, the effects that these changes will have on expected future electricity consumption patterns, as well as the effects on potential demand flexibility in a future where working from home has become the new norm.
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| 9. |
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| 10. |
- Zainali, Sebastian, 1995-, et al.
(författare)
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Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product
- 2023
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Ingår i: Energy and AI. - 2666-5468. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Gridded global horizontal irradiance (GHI) databases are fundamental for analysing solar energy applications' technical and economic aspects, particularly photovoltaic applications. Today, there exist numerous gridded GHI databases whose quality has been thoroughly validated against ground-based irradiance measurements. Nonetheless, databases that generate data at latitudes above 65˚ are few, and those available gridded irradiance products, which are either reanalysis or based on polar orbiters, such as ERA5, COSMO-REA6, or CM SAF CLARA-A2, generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites. Amongst the high-latitude gridded GHI databases, the STRÅNG model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is likely the most accurate one, providing data across Sweden. To further enhance the product quality, the calibration technique called "site adaptation" is herein used to improve the STRÅNG dataset, which seeks to adjust a long period of low-quality gridded irradiance estimates based on a short period of high-quality irradiance measurements. This study introduces a novel approach for site adaptation of solar irradiance based on machine learning techniques, which differs from the conventional statistical methods used in previous studies. Seven machine-learning algorithms have been analysed and compared with conventional statistical approaches to identify Sweden's most accurate algorithms for site adaptation. Solar irradiance data gathered from three weather stations of SMHI is used for training and validation. The results show that machine learning can substantially improve the STRÅNG model's accuracy. However, due to the spatiotemporal heterogeneity in model performance, no universal machine learning model can be identified, which suggests that site adaptation is a location-dependant procedure.
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