A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

Kim, Seul Gi; Jung, Jae‐Yoon; Sim, Min Kyu

doi:10.3390/su11051501

Cited by 88 publications

(42 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The method is based on historical weather data; there is a high correlation between the weather conditions in the present or past, and the solar power generation in the future. For solar power estimation, artificial neural networks, support vector machine (SVM), and machine learning have been utilized [36][37][38]. Techniques that use Long Short-Term Memory (LSTM), a time-series analysis method for weather data [39,40], as well as techniques that use both the past and current weather data, have also been proposed.…”

Section: Solar Power Estimation and Inverter Efficiency Analysismentioning

confidence: 99%

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Park¹,

Hong²,

Lim³

et al. 2020

Processes

View full text Add to dashboard Cite

The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.

show abstract

Section: Solar Power Estimation and Inverter Efficiency Analysismentioning

confidence: 99%

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Park¹,

Hong²,

Lim³

et al. 2020

Processes

View full text Add to dashboard Cite

show abstract

“…The prediction results of the forecasting models are evaluated using the mean absolute error (MAE) [29], root means squire error (RMSE) and the r 2 metrics [30]. r 2 is a statistical performances metric that gives a measure of the closeness between the actual and predicted data.…”

Section: Forecasting Error Metricsmentioning

confidence: 99%

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

et al. 2019

View full text Add to dashboard Cite

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

show abstract

“…The load forecasts help to identify strategies to optimize the operating mechanisms in a determined period and thus ensure the demand even in situations adverse to the system [ 1 ]. Accompanying the rapid advances in forecasting theory [ 2 , 3 ] and machine learning [ 4 , 5 , 6 ], the technology in the energy forecasting research area has also developed rapidly [ 7 ]. Additionally, the popular prediction methods for the generation and demand of energy can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas

Allende‐Cid

Salas

2020

Entropy

View full text Add to dashboard Cite

Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.

show abstract

A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

Cited by 88 publications

References 34 publications

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Contact Info

Product

Resources

About