Deep learning approach for one-hour ahead forecasting of weather data

Creative Commons License

Ozbek A.

Energy Sources, Part A: Recovery, Utilization and Environmental Effects, cilt.45, sa.3, ss.7606-7628, 2023 (SCI-Expanded)

Özet

Weather is made up of multiple parameters, including solar radiation (SR),

atmospheric pressure (AP), soil temperature (ST), atmospheric temperature

(AT), wind speed (WS), relative humidity (RH), and sunshine duration (SD).

These factors are also crucial for the renewable energy sector, solar simulation,

agriculture, air pollution, water supply and distribution, avalanche

warning, forestry, and town and regional planning. A deep learning method

based on a neural network with Long Short-Term Memory (LSTM) was

employed in this investigation for one-hour-ahead weather data forecasting.

The ability of the LSTM model was compared with the Adaptive Neuro-Fuzzy

Inference System (ANFIS) with that of the fuzzy c-means (FCM),

Autoregressive Integrated Moving Average (ARIMA) model, and the

Autoregressive Moving Average (ARMA) model. Mean absolute error (MAE),

correlation coefficient (R), root means square error (RMSE), average bias,

Nash – Sutcliffe efficiency coefficient (NSE), and mean absolute percentage

error (MAPE) were selected as evaluation criteria. Results indicated that the

proposed LSTM model presented good enough results compared to other

used methods. 7 different types of meteorological data from a total of 4 years

(35040 hours) were divided into 25% test data and 75% training data for the

models. The best result was obtained for the hourly ST estimation of Adana

province using the LSTM method, the MAE, RMSE, R, bias, NSE, and MAPE

values were computed as 0.016°C, 0.078°C, 0.9999, −0.00018°C, 0.0805%, and

0.9999, respectively. On the other hand, the worst result was obtained for the

hourly SD for Mardin province when ARIMA was used, and the statistical

measures were derived as 0.128 hours for MAE, 0.215 hours for RMSE, 0.8851

for R, 0.00091 hours for bias, and 0.7657 for NSE. In this regard, it is demonstrated

that the LSTM technique outperformed the other models in terms of

all-weather data estimates and delivered highly sensitive outcomes.