A method to estimate residential PV generation from net-metered load data and system install date

Stainsby, Wendell; Zimmerle, Daniel; Duggan, Gerald P.

doi:10.1016/j.apenergy.2020.114895

Cited by 30 publications

(13 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data-driven approaches can be divided Energies 2022, 15, 1312 2 of 18 into different types, based on the availability of historical measurement data, i.e., supervised, semi-supervised, and unsupervised methods. While supervised or semi-supervised methods necessitate all or a subset of historical PV power generation and load data from load customers [6][7][8][9][10][11], unsupervised approaches are based primarily on real-time power measurements [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Estimating Invisible Solar Power Generation

Lai

Huang

et al. 2022

Energies

View full text Add to dashboard Cite

In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Estimating Invisible Solar Power Generation

Lai

Huang

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…Shaker et al [15][16] proposed a method to estimate the invisible solar power generation based on the assumption that the measure data is available for a limited period of time for a large set of PV sites in order to select a small number of representative one. Stainsby et al [17] proposed a method to isolate solar generation from net load based on the array azimuth and title, inverter model and date of installation. This may not be feasible for countries like Europe, where the exact location, characteristics of individual small-scale solar PV installations and the associated generation data are generally withheld information due to general data protection legislations [4].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Estimation of Behind-the-Meter Solar and Wind Power at the Bulk Supply Point

et al. 2022

View full text Add to dashboard Cite

The presence of distributed energy resources in private premises has observed a large deployment worldwide driven by favorable governmental policies and technology development. In Northern Ireland, the small-scale generation capacity including solar and wind power has fast-grown accounting for over 17% of peak load in 2021, which makes net demands more volatile and diminishes the accuracy of electric demand forecasting activities. Different from traditional load forecasting methods based on the relationship between heterogeneous variables and net demand fluctuation, a novel approach is proposed to separately forecast the invisible components (e.g. wind & solar power) within the net demand. The critical challenge in the separate forecasting is how to extract behind-the-meter components from the measured net demand data, namely net demand disaggregation, for which continuous wavelet transform is applied to bring out the detailed fluctuation of net demand and an iterative search algorithm is proposed to separate the components in the net demand. The results reported show that the proposed demand disaggregation method contributes to reduce the forecasting error by more than 14%. Methods like the proposed net demand disaggregation can benefit utilities and system operators in performing their daily activities regarding load forecasting in serving areas and power systems they operate.

show abstract

“…The studies by [15], [16], and [17] all leverage unsupervised net load disaggregation methods. The net load disaggregation problem is formulated as an optimization and a signal separation problem in [15].…”

Section: Introductionmentioning

confidence: 99%

“…An unsupervised algorithm is developed in [16] have a common point of coupling. The algorithm proposed by [17] estimates electric load by comparing periods before PV installation with similar periods after PV installation that have common weather and activity characteristics and thereby perform net load disaggregation. Although pure data-driven methods have achieved some success, they are incapable of estimating the technical parameters of solar PV systems such as the tilt and DC size of the solar panel.…”

Section: Introductionmentioning

confidence: 99%

“…Some datadriven methods, such as [11], [12] only provide estimates of aggregate solar PV generation instead of the solar PV generation estimates for individual sites. The net load disaggregation algorithm proposed by [17] is built under the assumption that energy consumption habits do not significantly change once PV is installed, which may not always be true. Moreover, changes in the appliance mix or ownership of the house may also impact load patterns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Estimation of Behind-the-Meter Solar Generation in a Community

Kabir

Yao

et al. 2021

IEEE Trans. Sustain. Energy

View full text Add to dashboard Cite

A method to estimate residential PV generation from net-metered load data and system install date

Cited by 30 publications

References 20 publications

Artificial Intelligence Applications in Estimating Invisible Solar Power Generation

Artificial Intelligence Applications in Estimating Invisible Solar Power Generation

Simultaneous Estimation of Behind-the-Meter Solar and Wind Power at the Bulk Supply Point

Joint Estimation of Behind-the-Meter Solar Generation in a Community

Contact Info

Product

Resources

About