Daily Insolation Forecasting Using a Multi-Stage Neural Network

Kemmoku, Yoshishige; Orita, Shinichirou; Nakagawa, S.; Sakakibara, Tateki

doi:10.1016/s0038-092x(99)00017-1

Cited by 112 publications

(42 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(2) The monthly prediction curve is obtained by averaging the solar radiation in each pattern. (3) The prediction curve is selected depending on the weather forecast on the day.…”

Section: A Day-ahead Prediction Of Solar Radiationmentioning

confidence: 99%

Corrective Day-ahead Prediction of Solar Radiation based on Online Measurements

Kobayashi¹,

Watanabe²,

Hida³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

Abstract. In recent years, reduction of greenhouse gas emissions has been a serious issue throughout the world. The Japanese Government aims to reduce 25% of the emissions in 1990 by 2020. Eco-cities which effectively use renewable energy sources attract an attention in order to attain this goal. A smart energy town project in Honjyo, Japan has been conducted by making use of renewable energy sources. Above all, photovoltaic (PV) generators are expected to play a key role in this project. In addition, PV generators are expected to be installed in the residential and commercial areas due to ease of its installation. However, unstable power output from PV generation causes negative impacts on power quality of distribution networks. Therefore, prediction of PV output is indispensable to operate networks properly. In this paper, we propose a method to predict a day-ahead solar radiation and output power of PV generation in specific areas. Furthermore, the predicted solar radiation is corrected using the on-line measured actual data. As a result, the correction of a day-ahead prediction proposed here reduces errors of daily solar radiation by around 34% .

show abstract

“…(2) The monthly prediction curve is obtained by averaging the solar radiation in each pattern. (3) The prediction curve is selected depending on the weather forecast on the day.…”

Section: A Day-ahead Prediction Of Solar Radiationmentioning

confidence: 99%

Corrective Day-ahead Prediction of Solar Radiation based on Online Measurements

Kobayashi¹,

Watanabe²,

Hida³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

show abstract

“…NN has been the most widely used forecasting machine in solar forecasting, and it was first used to forecast solar irradiance based on weather data [9]. Furthermore, different types of NN, such as the feed-forward, recurrent Elman, and radial basis NN, were also used to forecast the hour ahead solar irradiance based on the cloud index [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

Baek

Lee

2017

Energies

View full text Add to dashboard Cite

Total daily solar irradiation for the next day is forecasted through an ensemble of multiple machine learning algorithms using forecasted weather scenarios from numerical weather prediction (NWP) models. The weather scenarios were predicted at grid points whose longitudes and latitudes are integers, but the total daily solar irradiation was measured at non-integer grid points. Therefore, six interpolation functions are used to interpolate weather scenarios at non-integer grid points, and their performances are compared. Furthermore, when the total daily solar irradiation for the next day is forecasted, many data trimming techniques, such as outlier detection, input data clustering, input data pre-processing, and output data post-processing techniques, are developed and compared. Finally, various combinations of these ensemble techniques, different NWP scenarios, and machine learning algorithms are compared. The best model is to combine multiple forecasting machines through weighted averaging and to use all NWP scenarios.

show abstract

“…For the a-day-ahead forecast with hourly resolution, the study in [56] to the severe transitions in the solar irradiance and weather change by a diurnal cycle. The studies in [57] and [58] also have developed solar irradiance forecast hour-by-hour or dayby-day using ANN. However, they produced results with large errors given the previous poorly forecasted value.…”

Section: Literature Reviewmentioning

confidence: 99%