An Hour-Ahead Photovoltaic Power Forecasting Based on LSTM Model

Kothona, Despoina; Panapakidis, Ioannis P.; Christoforidis, Georgios C.

doi:10.1109/powertech46648.2021.9494841

Cited by 4 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The output layer gives the output predictive result based on the analysis of the hidden layers. The contributions of the works [30–56] that propose ANN‐based SPF are presented in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…The forecasting horizon typically ranges from 1 h to 1 day [93]. Reviewed works [30, 32–36, 38, 39, 41–45, 47, 48, 59–63, 71, 75, 81, 82, 84] focus on short‐term SPF by using different models and methodologies.…”

Section: Methodologies and Data Analysismentioning

confidence: 99%

“…The reviewed works [18–84] focus on providing accurate short‐term SPF models. Those models take into account specific factors in order to achieve accurate forecasts.…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Bazionis,

Kousounadis‐Knousen,

Georgilakis

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models’ architecture and their field of focus.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodologies and Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Bazionis,

Kousounadis‐Knousen,

Georgilakis

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…Due to the structure of LSTM units, the LSTM model has the ability to capture dependencies between different time‐series and keep information from previous time‐steps to predict the target value [30]. One efficient PV forecasting methodology based on the LSTM model could be found in [31]. The day‐ahead load demand forecast consists of two parts.…”

Section: Problem Formulationmentioning

confidence: 99%

Optimal scheduling of prosumer's battery storage and flexible loads for distribution network support

Kelepouris

Nousdilis

Bouhouras

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

Here, an efficient Energy Management System (EMS) for residential installations is proposed. The EMS handles on site Photovoltaic (PV) power production along with Battery Energy Storage System (BESS) and performs load shifting under a Demand Side Management (DSM) scheme that considers user comfort. The goal of the proposed methodology is the minimization of power exchange between the prosumer and the grid. In this way, the power flow across the Distribution Network (DN) is decreased, thus reducing power losses, and improving the voltage profile along the DN. Moreover, the EMS increases the self-sufficiency of the residence. The minimization problem is solved via a metaheuristic algorithm, namely Unified Particle Swarm Optimization (UPSO) utilizing real consumption patterns for residential appliances. The latter adds complexity to the problem but offers accurate modelling of the power demand and improves the performance of the EMS scheme. The proposed EMS is locally implemented as a plug-and-play scheme and benefits both the prosumer and the DN under low computational burden, proving to be efficient. Simulation results for a case study verify the efficiency of the proposed EMS and the comparison with other similar methods proves its superior performance.

show abstract

“…The examined parameters were solar irradiance, PV module temperature, historical PV data, and clearness index. The results indicated that the inclusion of the clearness index as input can improve the performance of the forecaster [15]. Omar Nour-Eddine et al studied PV power production data of a 5.94 kWp grid-connected PV plant in Morocco.…”

Section: Introductionmentioning

confidence: 99%

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Ρουμπακιάς

Stamatelos

2022

Applied Sciences

View full text Add to dashboard Cite

Increased penetration of grid-connected PV systems in modern electricity networks induces uncertainty factors to be considered from several different viewpoints, including the system’s protection and management. Accurate short-term prediction of a grid-connected PV park’s output is essential for optimal grid control and grid resilience. Out of the numerous types of models employed to this end during the last decade, artificial neural networks, (ANNs) have proven capable of handling the uncertainty issues of solar radiation. Insolation and ambient, or panel temperature, are most commonly employed as the independent variables, and the system’s output power is successfully predicted within 3 to 5% error. In this paper, we apply a common type of ANN for the long-term prediction of a 100 kWp grid-connected PV park’s output, by exploiting experimental data from the last 8 years of operation. Solar radiation and backsheet temperature were utilized for the ANN training stage. The performance metrics of this model, along with a standard linear regression model, are compared against the actual performance data. The capabilities of the ANN model are exploited in the effort to decouple the fluctuating effect of PV panel soiling which interferes with the efficiency degradation process. The proposed methodology aimed to quantify degradation effects and is additionally employed as a fault diagnosis tool in long-term analysis.

show abstract

An Hour-Ahead Photovoltaic Power Forecasting Based on LSTM Model

Cited by 4 publications

References 19 publications

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Optimal scheduling of prosumer's battery storage and flexible loads for distribution network support

Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data

Contact Info

Product

Resources

About