uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 100) Show all publications
van der Meer, D. W., Shepero, M., Svensson, A., Widén, J. & Munkhammar, J. (2018). Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes. Applied Energy, 213, 195-207
Open this publication in new window or tab >>Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes
Show others...
2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 213, p. 195-207Article in journal (Refereed) Published
Abstract [en]

This paper presents a study into the utilization of Gaussian Processes (GPs) for probabilistic forecasting of residential electricity consumption, photovoltaic (PV) power generation and net demand of a single household. The covariance function that encodes prior belief on the general shape of the time series plays a vital role in the performance of GPs and a common choice is the squared exponential (SE), although it has been argued that the SE is likely suboptimal for physical processes. Therefore, we thoroughly test various (combinations of) covariance functions. Furthermore, in order bypass the substantial learning and inference time accompanied with GPs, we investigate the potential of dynamically updating the hyperparameters using a moving training window and assess the consequences on predictive accuracy. We show that the dynamic GP produces sharper prediction intervals (PIs) than the static GP with significant lower computational burden, but at the cost of the ability to capture sharp peaks. In addition, we examine the difference in accuracy between a direct and indirect forecasting strategy in case of net demand forecasting and show that the latter is prone to producing wider PIs with higher coverage probability.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-340597 (URN)10.1016/j.apenergy.2017.12.104 (DOI)000425576900017 ()
Funder
Swedish Energy Agency
Available from: 2018-01-28 Created: 2018-02-01 Last updated: 2018-04-25Bibliographically approved
Shepero, M., van der Meer, D., Munkhammar, J. & Widén, J. (2018). Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data. Applied Energy, 218, 159-172
Open this publication in new window or tab >>Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data
2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 218, p. 159-172Article in journal (Refereed) Published
Abstract [en]

Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.

Keywords
Gaussian process, Probabilistic load forecasting, Residential load
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
urn:nbn:se:uu:diva-345282 (URN)10.1016/j.apenergy.2018.02.165 (DOI)000430994500014 ()
Funder
Swedish Energy Agency
Available from: 2018-03-08 Created: 2018-03-08 Last updated: 2018-08-02Bibliographically approved
van der Meer, D., Widén, J. & Munkhammar, J. (2018). Review on probabilistic forecasting of photovoltaic power production and electricity consumption. Renewable & sustainable energy reviews, 1484-1512
Open this publication in new window or tab >>Review on probabilistic forecasting of photovoltaic power production and electricity consumption
2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, p. 1484-1512Article, review/survey (Refereed) Published
Abstract [en]

Accurate forecasting simultaneously becomes more important and more challenging due to the increasing penetration of photovoltaic (PV) systems in the built environment on the one hand, and the increasing stochastic nature of electricity consumption, e.g., through electric vehicles (EVs), on the other hand. Until recently, research has mainly focused on deterministic forecasting. However, such forecasts convey little information about the possible future state of a system and since a forecast is inherently erroneous, it is important to quantify this error. This paper therefore focuses on the recent advances in the area of probabilistic forecasting of solar power (PSPF) and load forecasting (PLF). The goal of a probabilistic forecast is to provide either a complete predictive density of the future state or to predict that the future state of a system will fall in an interval, defined by a confidence level. The aim of this paper is to analyze the state of the art and assess the different approaches in terms of their performance, but also to what extent these approaches can be generalized so that they not only perform best on the data set for which they were designed, but also on other data sets or different case studies. In addition, growing interest in net demand forecasting, i.e., demand less generation, is another important motivation to combine PSPF and PLF into one review paper and assess compatibility. One important finding is that there is no single preferred model that can be applied to any circumstance. In fact, a study has shown that the same model, with adapted parameters, applied to different case studies performed well but did not excel, when compared to models that were optimized for the specific task. Furthermore, there is need for standardization, in particular in terms of filtering night time data, normalizing results and performance metrics. 

National Category
Energy Systems
Identifiers
urn:nbn:se:uu:diva-332709 (URN)10.1016/j.rser.2017.05.212 (DOI)000417070500106 ()
Funder
Swedish Energy Agency
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-03-12Bibliographically approved
Munkhammar, J., Widén, J. & Hinkelman, L. M. (2017). A copula method for simulating correlated instantaneous solar irradiance in spatial networks. Solar Energy, 143, 10-21
Open this publication in new window or tab >>A copula method for simulating correlated instantaneous solar irradiance in spatial networks
2017 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 143, p. 10-21Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Engineering and Technology Environmental Engineering
Identifiers
urn:nbn:se:uu:diva-316519 (URN)10.1016/j.solener.2016.12.022 (DOI)000393246100002 ()
Funder
Swedish Energy Agency
Available from: 2017-03-02 Created: 2017-03-02 Last updated: 2017-11-29Bibliographically approved
Munkhammar, J. & Widén, J. (2017). An autocorrelation-based copula model for generating realistic clear-sky index time-series. Solar Energy, 158, 9-19
Open this publication in new window or tab >>An autocorrelation-based copula model for generating realistic clear-sky index time-series
2017 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 158, p. 9-19Article in journal (Refereed) Published
Abstract [en]

This study presents a method for using copulas to model the temporal variability of the clear-sky index, which in turn can be used to produce realistic time-series of photovoltaic power generation. The method utilizes the autocorrelation function of a clear-sky index time-series, and based on that a correlation matrix is set up for the dependency between clear-sky indices at Ntime-steps. With the use of this correlation matrix an N-dimensional copula function is configured so that correlated samples for these N time-steps can be obtained. Results from the copula model are compared with the original data set and an uncorrelated model based on zero correlated clear-sky index data in terms of distribution, autocorrelation, step changes and distribution. The copula model is shown to be superior to the uncorrelated model in these aspects. As a validation the model is tested with solar irradiance for two different geographical regions: Norrköping, Sweden and Hawaii, USA. The copula model is also applied to a set of bins of daily mean clear-sky index and the use of bins is shown to improve the results.

Keywords
Autocorrelation function, Copula modeling, Probability distribution modeling
National Category
Probability Theory and Statistics Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-330876 (URN)10.1016/j.solener.2017.09.028 (DOI)000418974500002 ()
Available from: 2017-10-05 Created: 2017-10-05 Last updated: 2018-02-27Bibliographically approved
Munkhammar, J. & Widén, J. (2017). An autocorrelation-based copula model for producing realistic clear-sky index and photovoltaic power generation time-series. In: Photovoltaic specialist conference: . Paper presented at 2017 IEEE 44th Photovoltaic Specialists Conference (PVSC), 25 Jun - 30 Jun 2017, Washington, USA (pp. 1-6). Washington
Open this publication in new window or tab >>An autocorrelation-based copula model for producing realistic clear-sky index and photovoltaic power generation time-series
2017 (English)In: Photovoltaic specialist conference, Washington, 2017, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

This study presents a method for using copulas to model the temporal variability of the clear-sky index. The method utilizes the autocorrelation function and correlated outputs for N time-steps are obtained. Results from the copula model are, in terms of distribution, autocorrelation, step changes and mean daily distribution, compared with the original data set and with an uncorrelated model based on random clear-sky index data. The copula model is shown to be superior to the uncorrelated model in all these aspects.

Place, publisher, year, edition, pages
Washington: , 2017
Keywords
autocorrelation function, clear-sky index, copula modeling, distribution modeling. realistic time-series.
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-327345 (URN)
Conference
2017 IEEE 44th Photovoltaic Specialists Conference (PVSC), 25 Jun - 30 Jun 2017, Washington, USA
Available from: 2017-08-09 Created: 2017-08-09 Last updated: 2017-08-10Bibliographically approved
Lingfors, D., Bright, J. M., Engerer, N. A., Ahlberg, J., Killinger, S. & Widén, J. (2017). Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis. Applied Energy, 205, 1216-1230
Open this publication in new window or tab >>Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis
Show others...
2017 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 205, p. 1216-1230Article in journal (Refereed) Published
Abstract [en]

LiDAR (Light Detection and Ranging) data have recently gained popularity for use in solar resource assessment and solar photovoltaics (PV) suitability studies in the built environment due to robustness at identifying building orientation, roof tilt and shading. There is a disparity in the geographic coverage of low- and high-resolution LiDAR data (LL and LH, respectively) between rural and urban locations, as the cost of the latter is often not justified for rural areas where high PV penetrations often pose the greatest impact on the electricity distribution network. There is a need for a comparison of the different resolutions to assess capability of LL. In this study, we evaluated and improved upon a previously reported methodology that derives roof types from a LiDAR-derived, low-resolution Digital Surface Model (DSM) with a co-classing routine. Key improvements to the methodology include: co-classing routine adapted for raw LiDAR data, applicability to differing building type distribution in study area, building height and symmetry considerations, a vector-based shading analysis of building surfaces and the addition of solar resource assessment capability.

Based on the performance of different LiDAR resolutions within the developed model, a comparison between LL (0.5-1 pts/m(2)) and LH (6-8 pts/m(2)) LiDAR data was applied; LH can confidently be used to evaluate the applicability of LL, due to its significantly higher point density and therefore accuracy. We find that the co-classing methodology works satisfactory for LL for all types of building distributions. Roof-type identification errors from incorrect co-classing were rare (< 1%) with LL. Co-classing buildings using LL improves accuracy of roof-type identification in areas with homogeneous distribution of buildings, here from 78% to 86% in accuracy. Contrastingly, co-classing accuracy using LH is marginally reduced for all building distributions from 94.8% to 94.4%. We adapt the Hay and Davies solar transposition model to include shading. The shading analysis demonstrates similarity of results between LL and LH. We find that the proposed methodology can confidently be used for solar resource assessments on buildings when only LiDAR data of low-resolution (< 1 pts/m(2)) is available.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
LiDAR, Solar resource assessment, Shading, Building classification, Low-resolution, High-resolution
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-332226 (URN)10.1016/j.apenergy.2017.08.045 (DOI)000414817100098 ()
Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2018-02-20Bibliographically approved
Luthander, R., Lingfors, D. & Widén, J. (2017). Large-scale integration of photovoltaic power in a distribution grid using power curtailment and energy storage. Solar Energy, 155, 1319-1325
Open this publication in new window or tab >>Large-scale integration of photovoltaic power in a distribution grid using power curtailment and energy storage
2017 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 155, p. 1319-1325Article in journal (Refereed) Published
Keywords
Photovoltaics, Power distribution system, Energy storage, Power and voltage control, Overvoltage
National Category
Energy Engineering
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-328066 (URN)10.1016/j.solener.2017.07.083 (DOI)000414819900057 ()
Projects
Småskalig solel i byggnader – kraft för förändring i energisystem och vardaglivetUtvärdering av tekniska lösningar för att hantera omfattande anslutning av solcellssystem i eldistributionsnät
Funder
Swedish Energy Agency, P37511-1
Note

Photovoltaic (PV) power generation is an important component for the future energy system. High penetrationof PV power in a power distribution system might however lead to problems with overvoltage and overload. In this study, a method for PV power curtailment and placement of decentralized energy storage is developed to control voltage, feeder currents and distribution substation overloading. The method determines an individual feed-in power limit for each PV system owner based on a voltage-power relationship. Measured data from a 10 kV/400 V three-phase distribution grid in the Swedish municipality of Herrljunga with more than 5000 end-users and simulated PV electricity production data are used for a case study to verify the model. The method is evaluated for yearly PV electricity productionof up to 100% of the yearly electricity consumption. The results show that the method is able to prevent overvoltage for all penetration levels in the studied distribution grid, reduce the number of feeders affected by overcurrent and lower the maximum load on the two substations.

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2018-02-23Bibliographically approved
Åberg, M., Fälting, L., Carlsson, J., Johansson, L., Forssell, A., Widén, J., . . . Lingfors, D. (2017). Nya lösningar för fjärrvärme i miljonprogramsområden. Stockholm: Energiforsk
Open this publication in new window or tab >>Nya lösningar för fjärrvärme i miljonprogramsområden
Show others...
2017 (Swedish)Report (Other academic)
Place, publisher, year, edition, pages
Stockholm: Energiforsk, 2017. p. 98
Series
Energiforsk rapport ; 2017:414
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-332984 (URN)978-91-7673-414-8 (ISBN)
Funder
Swedish Energy Agency, 39605-1
Available from: 2017-11-03 Created: 2017-11-03 Last updated: 2018-02-20Bibliographically approved
Widén, J., Shepero, M. & Munkhammar, J. (2017). On the properties of aggregate clear-sky index distributions and an improved model for spatially correlated instantaneous solar irradiance. Solar Energy, 157, 566-580
Open this publication in new window or tab >>On the properties of aggregate clear-sky index distributions and an improved model for spatially correlated instantaneous solar irradiance
2017 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 157, p. 566-580Article in journal (Refereed) Published
Abstract [en]

An important factor in grid integration of solar power is the so-called dispersion-smoothing effect, i.e., that differences in cloudiness over dispersed systems make the aggregate output less variable. This effect has been studied for irradiance step-changes on different time horizons, but not so much for instantaneous irradiance. In this paper, an improved probabilistic model is proposed for how instantaneous solar irradiance is correlated and aggregated over a network with arbitrary number of sites and dispersion. The model is fitted to irradiance data with a 1-s resolution from a network with 17 pyranometers in Hawai’i. A previously proposed three-state model of the instantaneous clear-sky index is partly confirmed by showing that clear and cloudy states can be separated and modeled by independent distribution models. It is also shown that the station-pair correlations for the instantaneous clear-sky index, as well as the shape of the distribution for the cloudy states, depend characteristically on the average degree of cloudiness, represented by the daily clear-sky index. For dispersed sites within the studied network, separated by distances up to 1km, and for daily clear-sky indices above approximately 0.5, the model performs better in reproducing the aggregate clear-sky index than non-spatial data. The proposed model could assist distribution system operators (DSOs) in grid planning and operation, as shown in a case study.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Solar irradiance, Clear-sky index, Modelling, Correlation
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-333351 (URN)10.1016/j.solener.2017.08.033 (DOI)000418314500055 ()
Available from: 2017-11-10 Created: 2017-11-10 Last updated: 2018-01-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4887-9547

Search in DiVA

Show all publications