| 1. |
- Shepero, Mahmoud, 1992-, et al.
(författare)
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A generative hidden Markov model of the clear-sky index
- 2019
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Ingår i: Journal of Renewable and Sustainable Energy. - : AIP Publishing. - 1941-7012. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- Clear-sky index (CSI) generative models are of paramount importance in, e.g., studying the integration of solar power in the electricity grid. Several models have recently been proposed with methodologies that are related to hidden Markov models (HMMs). In this paper, we formally employ HMMs, with Gaussian distributions, to generate CSI time-series. The authors propose two different methodologies. The first is a completely data-driven approach, where an HMM with Gaussian observation distributions is proposed. In the second, the means of these Gaussian observation distributions were predefined based on the fraction of time of bright sunshine from the site. Finally, the authors also propose a novel method to improve the autocorrelation function (ACF) of HMMs in general. The two methods were tested on two datasets representing two different climate regions. The performance of the two methodologies varied between the two datasets and among the compared performance metrics. Moreover, both the proposed methods underperformed in reproducing the ACF as compared to state-of-the-art models. However, the method proposed to improve the ACF was able to reduce the mean absolute error (MAE) of the ACF by up to 19%. In summary, the proposed models were able to achieve a Kolmogorov-Smirnov test score as low as 0.042 and MAE of the ACF as low as 0.012. These results are comparable with the state-of-the-art models. Moreover, the proposed models were fast to train. HMMs are shown to be viable CSI generative models. The code for the model and the simulations performed in this paper can be found in the GitHub repository: HMM-CSI-generativeModel.
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| 2. |
- van der Meer, Dennis, et al.
(författare)
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Probabilistic forecasting of solar power, electricity consumption and net load : Investigating the effect of seasons, aggregation and penetration on prediction intervals
- 2018
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Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 171, s. 397-413
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a study into the effect of aggregation of customers and an increasing share of photovoltaic (PV) power in the net load on prediction intervals (PIs) of probabilistic forecasting methods applied to dis- tribution grid customers during winter and spring. These seasons are shown to represent challenging cases due to the increased variability of electricity consumption during winter and the increased variability in PV power production during spring. We employ a dynamic Gaussian process (GP) and quantile regression (QR) to produce probabilistic forecasts on data from 300 de-identified customers in the metropolitan area of Sydney, Australia. In case of the dynamic GP, we also optimize the training window width and show that it produces sharp and reliable PIs with a training set of up to 3 weeks. In case of aggregation, the results indicate that the aggregation of a modest number of PV systems improves both the sharpness and the reliability of PIs due to the smoothing effect, and that this positive effect propagates into the net load forecasts, especially for low levels of aggregation. Finally, we show that increasing the share of PV power in the net load actually increases the sharpness and reliability of PIs for aggregations of 30 and 210 customers, most likely due to the added benefit of the smoothing effect.
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| 3. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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A Markov-chain probability distribution mixture approach to the clear-sky index
- 2018
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Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 170, s. 174-183
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a Markov-chain probability distribution mixture approach to the clear-sky index (CSI). The main assumption is that the temporal variability of the state of clear and the state of cloudy can be described by a two-state Markov-chain, and the variability within each state can be approximated by a probability distribution, unique for each state. Measurables such as the mean clear-sky index, fraction of bright sunshine, expected duration of clearness and expected duration of cloudiness events are shown to be related to the parameters of the method. Additionally, the Ångström equation, which relates mean normalized solar irradiance to the fraction of bright sunshine, is shown to arise as the expectation of the method. In order to numerically verify the method, a simulation model is constructed based on data sets for two different climatic regions: Norrköping, Sweden and Oahu, Hawaii, USA. Results from the simulation model based on training data shows good agreement with testing data, and when comparing the results to existing models in the literature it is comparable to the state of the art. It is shown that the simulation model generates a non-trivial, generally non-zero, autocorrelation function. Finally, challenges with the method and open problems are discussed.
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| 4. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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A copula method for simulating correlated instantaneous solar irradiance in spatial networks
- 2017
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Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 143, s. 10-21
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a method for generating correlated instantaneous solar irradiance data for an arbitrary set of spatially dispersed locations. Based on the empirical clear-sky index distribution for one location and the cross-correlation between clear-sky index data at all location pairs, a copula is used to represent the dependence between locations. The method is primarily intended for probabilistic simulations of electricity distribution grids with high penetrations of photovoltaic (PV) systems, in which solar irradiance data for nodes in the grid can be sampled from the model. The method is validated against a 10-s resolution solar irradiance data set for 14 locations, dispersed within an array of approximately 1 km 1.2 km, at the Island of Oahu, Hawai’i, USA. The results are compared with previous results for along- and cross-wind pairs of locations, and with models for adjacent (completely correlated) and dispersed (completely uncorrelated) locations. It is shown that the copula approach performs better than the adjacent model for a majority of all location pairs and for all but one pair of locations separated more than 500 m. It outperforms the dispersed model for all pairs of locations. In conclusion, the proposed method can generate correlated data and estimate the aggregate clear-sky index for any set of locations based only on the distribution of the clear-sky index for a single location.
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| 5. |
- Berggren, Björn, et al.
(författare)
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Evaluation and optimization of a Swedish Net ZEB - Using load matching and grid interaction indicators
- 2012
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Ingår i: Proceedings of the First Building Simulation and Optimization Conference, Loughborough, UK, September 10-11, 2012.
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Konferensbidrag (refereegranskat)abstract
- Net Zero Energy Buildings, Net ZEBs, is one of many necessary measures for climate change mitigation as they may reduce the energy consumption in the building sector. The Net ZEB interacts with a grid infrastructure. It is therefore important to consider the interaction with the grid in the design phase. This paper reports an evaluation of a proposed design of a Net ZEB in the south of Sweden evaluating load matching and grid interaction using simulated data sets with hourly resolution. The aim was to find a design with as high load matching and as low grid interaction as possible. The results show difficulties of achieving a high load matching between the building load and on-site generation, due to the Nordic climate and the relatively low loads during daytime, when the availability of solar energy is high. The building is likely to accomplish the goal of a Net ZEB balance. If higher flexibility is sought, a larger energy storage should be considered.
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| 6. |
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| 7. |
- Dahlström, Lukas, et al.
(författare)
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Advancing urban building energy modelling through new model components and applications : A review
- 2022
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Ingår i: Energy and Buildings. - : Elsevier. - 0378-7788 .- 1872-6178. ; 266
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Forskningsöversikt (refereegranskat)abstract
- Due to rapid urbanisation and the significant contribution of cities to worldwide energy use and greenhouse gas emissions, urban energy system planning is growing more important. Urban building energy modelling (UBEM) draws increasing attention in the energy modelling field due to its inherent capacities for modelling entire cities or building stocks, and the potential of varying data inputs, approaches and applications. This review aims to identify best practices and improvements for UBEM applications by examining previous research, with a focus on the currently least established approaches. Different archetype development procedures are analysed for common problems, six main under-developed input approaches or parameters are identified, and applications for future scenario development are surveyed. By analysing previous studies in related fields, this paper provides an overview of gaps in the published research and possible additions to future UBEM projects that can help expanding the existing modelling procedures. Comprehensive human behaviour models with additional aspects beyond occupant presence are identified as a major point of interest. Further research on socio-economic parameters, such as household income and demographics, are also suggested to further improve modelling. This study also underlines the potential for utilising UBEM as a tool for evaluating future climate change scenarios.
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