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Related papers: Day-Ahead PV Power Forecasting Based on MSTL-TFT

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This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods…

Applications · Statistics 2015-08-12 Yubo Wang , Bin Wang , Rui Huang , Chi-Cheng Chu , Hemanshu R. Pota , Rajit Gadh

As the energy landscape changes quickly, grid operators face several challenges, especially when integrating renewable energy sources with the grid. The most important challenge is to balance supply and demand because the solar and wind…

Machine Learning · Computer Science 2025-01-24 Kamal Sarkar

Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also has a great interest to investors and energy policy maker as well as government. Literature reveals that 1% error drop of forecast can reduce 10…

Machine Learning · Computer Science 2021-11-02 Yanmei Huang , Najmul Hasan , Changrui Deng , Yukun Bao

Solar PV yield nowcasting is used to help anticipate peaks and troughs in demand to support grid integration. This paper compares multiple low-resource approaches to nowcasting solar PV yield, using a dataset of UK satellite imagery and…

Machine Learning · Computer Science 2022-10-11 Ben Dixon , María Pérez-Ortiz , Jacob Bieker

With the expected rise in behind-the-meter solar penetration within the distribution networks, there is a need to develop time-series forecasting methods that can reliably predict the net-load, accurately quantifying its uncertainty and…

Signal Processing · Electrical Eng. & Systems 2022-03-10 Deepthi Sen , Indrasis Chakraborty , Soumya Kundu , Andrew P. Reiman , Ian Beil , Andy Eiden

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand. In this study, we modeled an existing…

Renewable Energies (RE) have gained more attention in recent years since they offer clean and sustainable energy. One of the major sustainable development goals (SDG-7) set by the United Nations (UN) is to achieve affordable and clean…

Machine Learning · Computer Science 2022-12-27 Mehdi Hatamian , Bivas Panigrahi , Chinmaya Kumar Dehury

Solar energy is one of the most promising renewable energy resources. Forecasting photovoltaic power generation is an important way to increase photovoltaic penetration. However, the difficulty in qualifying the uncertainty of PV power…

Machine Learning · Computer Science 2024-10-10 Xuan Yang , Yunxuan Dong , Lina Yang , Thomas Wu

Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local…

Distributed, Parallel, and Cluster Computing · Computer Science 2014-10-28 Elizabeth Basha , Raja Jurdak , Daniela Rus

The integration of renewable energy sources (RES) into power grids presents significant challenges due to their intrinsic stochasticity and uncertainty, necessitating the development of new techniques for reliable and efficient forecasting.…

Machine Learning · Statistics 2024-09-13 Hanyu Zhang , Reza Zandehshahvar , Mathieu Tanneau , Pascal Van Hentenryck

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on…

Neural and Evolutionary Computing · Computer Science 2013-08-19 Giacomo Capizzi , Christian Napoli , Francesco Bonanno

This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series based environmental inputs. Generally, artificial neural network based MPPT algorithms use…

Systems and Control · Electrical Eng. & Systems 2024-09-26 Palaash Agrawal , Hari Om Bansal , Aditya R. Gautam , Om Prakash Mahela , Baseem Khan

Solar based electricity generations have experienced strong and impactful growth in recent years. The regulation, scheduling, dispatching, and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting…

Systems and Control · Electrical Eng. & Systems 2020-03-30 Shaktinarayana Mishra , Lokanath Tripathy , Prachitara Satapathy , P. K. Dash , Nitasha Sahani

The objective of the GreenPAD project is to use green energy (wind, solar and biomass) for powering data-centers that are used to run HPC jobs. As a part of this it is important to predict the Renewable (Wind) energy for efficient…

Machine Learning · Computer Science 2014-02-27 Ankur Sahai

The bifacial gain of East-West vertical and South-facing optimally-tilted bifacial farms are well established. One wonders if bifacial gain (and the associated LCOE) may be further improved by tracking the sun. Tracking bifacial PV has…

The rapid global expansion of solar photovoltaic (PV) capacity-reaching a record 597 GW in 2024-highlights the urgent need for robust forecasting models to mitigate the grid instability caused by the intermittent nature of solar irradiance.…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Sumit Laha , Ankit Sharma , Hassan Foroosh

Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected forecast horizons. However, these estimates allow only a…

Applications · Statistics 2019-06-03 Jens Schreiber , Artjom Buschin , Bernhard Sick

Wind energy is a widely distributed, renewable, and environmentally friendly energy source that plays a crucial role in mitigating global warming and addressing energy shortages. Nevertheless, wind power generation is characterized by…

Machine Learning · Computer Science 2023-09-06 Meiyu Jiang , Jun Shen , Xuetao Jiang , Lihui Luo , Rui Zhou , Qingguo Zhou

The challenge in solar energy today is not the cost of photovoltaic (PV) electricity generation, already competing with fossil fuel prices, but rather utility-scale energy storage and flexibility in supply. Low-cost thermal energy storage…

The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or…

Machine Learning · Computer Science 2020-07-17 Michela Moschella , Mauro Tucci , Emanuele Crisostomi , Alessandro Betti