Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts
In one sentence. An Elman neural network tuned by Mayfly Optimization predicts solar irradiance from weather forecast data, with Hadoop preprocessing. Results beat comparison methods.
Abstract
Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment. Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems. Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time. In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weather-related parameters such as temperature, humidity, wind speed, pressure, and precipitation. Therefore, in current study, the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models (BDAAI- SIP) using weather forecasting data. In order to perform long-term collection of weather data, Hadoop MapReduce tool is employed. The proposed solar irradiance prediction model operates on different stages. Primarily, data preprocessing take place using various sub processes such as data conversion, missing value replacement, and data normalization. Besides, Elman Neural Network (ENN), a type of feedforward neural network is also applied for predictive analysis. It is divided into input layer, hidden layer, load-bearing layer, and output layer. To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count, Mayfly Optimization (MFO) algorithm is applied. In order to validate the performance of the proposed model, a series of experiments was conducted. The experimental values infer that the proposed model outperformed other methods used for comparison.
Labels
- Topic
- Applications & impacts
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.32604/cmc.2022.021015 OpenAlex:W3210620853
- Added
- 2026-10-22
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