A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network

https://www.econstor.eu/bitstream/10419/187893/1/1-s2.0-S2352484717300616-main.pdf

Power systems big data analytics: An assessment of paradigm shift barriers and prospects

Demand side management of small scale loads in a smart grid using glow-worm swarm optimization technique

Disaggregating solar generation from feeder-level measurements

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F1-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

https://www.mdpi.com/1996-1073/14/9/2390/pdf

Review on Deep Neural Networks Applied to Low-Frequency NILM

We consider the problem of estimating the unobserved amount of photovoltaic (PV) generation and demand in a power distribution network starting from measurements of the aggregated power flow at the point of common coupling and local global horizontal irradiance (GHI). The estimation principle relies on modeling the PV generation as a function of the measured GHI, enabling the identification of PV production patterns in the aggregated power flow measurements. Four estimation algorithms are proposed: the first assumes that variability in the aggregated PV generation is given by variations of PV generation, the next two use a model of the demand to improve estimation performance, and the fourth assumes that, in a certain frequency range, the aggregated power flow is dominated by PV generation dynamics. These algorithms leverage irradiance transposition models to explore several azimuth/tilt configurations and explain PV generation patterns from multiple plants with nonuniform installation characteristics. Their estimation performance is compared and validated with measurements from a real-life setup including four houses with rooftop PV installations and battery systems for PV self-consumption.

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Deployment and analysis of a blockchain-based local energy market

In this paper, we present a method to determine the global horizontal irradiance (GHI) from the power measurements of one or more PV systems, located in the same neighborhood. The method is completely unsupervised and is based on a physical model of a PV plant. The precise assessment of solar irradiance is pivotal for the forecast of the electric power generated by photovoltaic (PV) plants. However, on-ground measurements are expensive and are generally not performed for small and medium-sized PV plants. Satellite-based services represent a valid alternative to on site measurements, but their space-time resolution is limited. Results from two case studies located in Switzerland are presented. The performance of the proposed method at assessing GHI is compared with that of free and commercial satellite services. Our results show that the presented method is generally better than satellite-based services, especially at high temporal resolutions.

An Unsupervised Method for Estimating the Global Horizontal Irradiance from Photovoltaic Power Measurements

An unsupervised method for estimating the global horizontal irradiance from photovoltaic power measurements

In the context of a pilot project, the Lugaggia Innovation Community (LIC), we address the problem of non-intrusive load monitoring for the purpose of demand side management on low voltage grids in presence of distributed power generation (photovoltaic). From the power readings of smart meters, we estimate the photovoltaic production and detect the activation of major loads (heatpumps and domestic water heaters). Experiments, conducted with real data and in silico, show that exploiting meter readings only, we can estimate PV production with MAPE ranging from 4.6% (best case) to 41.9% (worst case). Even with non negligible photovoltaic production estimation errors, the proposed method is capable of detecting the activation of heatpumps and domestic water heaters.

/pdf/non-intrusive-load-monitoring-for-demand-side-management-yq9x5ssvw6.pdf

Lorenzo Nespoli

Papers

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

Deployment and analysis of a blockchain-based local energy market

An Unsupervised Method for Estimating the Global Horizontal Irradiance from Photovoltaic Power Measurements

An unsupervised method for estimating the global horizontal irradiance from photovoltaic power measurements

Non intrusive load monitoring for demand side management