Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data

Solar irradiance prediction is significant for maximizing energy-saving effects in the predictive control of buildings. Several models for solar irradiance prediction have been developed; however, they require the collection of weather data over a long period in the predicted target region or evaluation of various weather data in real time. In this study, a long short-term memory algorithm–based model is proposed using limited input data and data from other regions. The proposed model can predict solar irradiance using next-day weather forecasts by the Korea Meteorological Administration and daily solar irradiance, and it is possible to build a model with one-time learning using national and international data. The model developed in this study showed excellent predictive performance with a coefficient of variation of the root mean square error of 12% per year even if the learning and forecast regions were different, assuming that the weather forecast was correct.

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Visibility Prediction over South Korea Based on Random Forest

Atmosphere ◽

10.3390/atmos12050552 ◽

2021 ◽

Vol 12 (5) ◽

pp. 552

Author(s):

Bu-Yo Kim ◽

Joo Wan Cha ◽

Ki-Ho Chang ◽

Chulkyu Lee

Keyword(s):

Random Forest ◽

South Korea ◽

Correlation Coefficients ◽

Predictive Performance ◽

Air Pollutant ◽

Economic Losses ◽

Observation Data ◽

Local Data ◽

Weather Forecasts ◽

Monitoring Service

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

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A Multistep Prediction of Hydropower Station Inflow Based on Bagging-LSTM Model

Discrete Dynamics in Nature and Society ◽

10.1155/2021/1031442 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Lulu Wang ◽

Hanmei Peng ◽

Mao Tan ◽

Rui Pan

Keyword(s):

Short Term Memory ◽

Southern China ◽

Time Lag ◽

Weather Data ◽

Hydropower Station ◽

Related Factors ◽

Joint Influence ◽

Proposed Model ◽

Long Short Term Memory ◽

Deep Learning Model

The inflow forecasting is one of the most important technologies for modern hydropower station. Under the joint influence of soil, upstream inflow, and precipitation, the inflow is often characterized by time lag, nonlinearity, and uncertainty and then results in the difficulty of accurate multistep prediction of inflow. To address the coupling relationship between inflow and the related factors, this paper proposes a long short-term memory deep learning model based on the Bagging algorithm (Bagging-LSTM) to predict the inflows of future 3 h, 12 h, and 24 h, respectively. To validate the proposed model, the inflow and related weather data come from a hydropower station in southern China. Compared with the classical time series models, the results show that the proposed model outperforms them on different accuracy metrics, especially in the scenario of multistep prediction.

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Some Interim Results of the Study

Journal of Navigation ◽

10.1017/s0373463300021901 ◽

1982 ◽

Vol 35 (3) ◽

pp. 502-516

Author(s):

R. Monk

Keyword(s):

North Atlantic ◽

Weather Forecast ◽

Air Traffic ◽

Weather Data ◽

Weather Forecasts ◽

Additional Information ◽

European Cities ◽

The North ◽

The North Atlantic ◽

To Receive

We are still in the process of collecting and developing ways of studying and analysing air traffic routes across the North Atlantic. The results presented in this paper must therefore be recognized as provisional. The data comprise some twelve examples of North Atlantic weather forecasts issued from Bracknell; they are sent to us regularly for the 2nd and 15th day of each month. We have also made arrangements to receive notification from the Heathrow Meteorological Office of any days in which there were significant changes in the weather forecast, so that we can request the additional information from Bracknell. Each set of weather data contains the ‘analysis weather’, that is the best estimate of the actual weather at 1200 GMT, and therefore applicable to the time when aircraft are making westerly departures across the North Atlantic from European cities, and also the weather forecasts issued for 12 and 24 hours before this time.

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Resolution of Probabilistic Weather Forecasts with Application in Disease Management

Phytopathology ◽

10.1094/phyto-07-16-0256-r ◽

2017 ◽

Vol 107 (2) ◽

pp. 158-162 ◽

Cited By ~ 2

Author(s):

G. Hughes ◽

N. McRoberts ◽

F. J. Burnett

Keyword(s):

Disease Management ◽

Mutual Information ◽

Disease Risk ◽

Information Gain ◽

Weather Forecast ◽

Weather Data ◽

Specific Information ◽

Information Theoretic ◽

Weather Forecasts ◽

Measuring Change

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

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Weather forecasts - the islands of calmness in the rough TV sea

10.5194/ems2021-407 ◽

2021 ◽

Author(s):

Stanislava Tsalova

Keyword(s):

Positive Emotions ◽

Scientific Information ◽

Weather Forecast ◽

Weather Data ◽

Stress Release ◽

Weather Forecasts ◽

The Past ◽

The People ◽

Tv Ratings ◽

Tv Shows

People who are not involved in doing Weather forecast presentations, think that it is something easy to prepare. But it needs experience to present&#160;the weather data and forecast, which is scientific information&#160;in a way understandable for the TV viewers.&#160;Weather forecasts have always been islands of positive emotions in TV programs.&#160;The past year was very challeging for all TV stations around the world. In all the news and TV shows the main topic was Coronavirus disease.&#160;Now, more than ever TV weather forecast's role became to provide some positive emotions to the people who are so much got tired of the bad and scary news on their TVs. The fact is that during the pandemic the TV ratings are higher made our responsibility even bigger. While preparing my weather presentations, even in cases of severe weather my top priority was not to scare people, who were scared enough. When showing weather videos, I avoided such with disasters. Instead I showed more wildlife and educational weather videos. Unlike before, in 2020/2021 years I definitely avoided climate change topic.&#160; While chatting about weather on air with the news and morning shows anchors, the chat had sometimes escalated to bursting into laughter. Unlike before, our viewers approved that highly, because everybody is under pressure now and such stress release things were more than welcome. The weather forecast now became more than ever an island of calmness and hope for a better tomorrow in the rough TV sea. I want to share my experience and to exchange opinion on that topic with collegues from other countries and TV stations.

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Precipitation Nowcasting using Deep Neural Network

10.5194/egusphere-egu2020-21631 ◽

2020 ◽

Author(s):

Mohamed Chafik Bakey ◽

Mathieu Serrurier

Keyword(s):

Traffic Management ◽

Prediction Models ◽

Short Term Memory ◽

Weather Prediction ◽

Weather Forecast ◽

Proposed Model ◽

Anticipation Time ◽

Memory Network ◽

Convolutional Encoder

Precipitation nowcasting is the prediction of the future precipitation rate in a given geographical region with an anticipation time of a few hours at most. It is of great importance for weather forecast users, for activitites ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical weather prediction models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. In this work, we develop an original deep learning approach. We formulate precipitation nowcasting issue as a video prediction problem where both input and prediction target are image sequences. The proposed model combines a Long Short-Term Memory network (LSTM) with a convolutional encoder-decoder network (U-net). Experiments show that our method captures spatiotemporal correlations and yields meaningful forecasts

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Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Energies ◽

10.3390/en12071309 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1309 ◽

Cited By ~ 3

Author(s):

Eva Lucas Segarra ◽

Hu Du ◽

Germán Ramos Ruiz ◽

Carlos Fernández Bandera

Keyword(s):

Energy Demand ◽

Forecast Error ◽

Weather Forecast ◽

Building Energy ◽

Weather Data ◽

Weather Forecasts ◽

Hourly Data ◽

The Future ◽

The Impact ◽

Near Future

The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

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Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

Atmosphere ◽

10.3390/atmos10110718 ◽

2019 ◽

Vol 10 (11) ◽

pp. 718 ◽

Cited By ~ 2

Author(s):

Park ◽

Kim ◽

Lee ◽

Kim ◽

Song ◽

...

Keyword(s):

Neural Network ◽

Missing Data ◽

Prediction Model ◽

Short Term Memory ◽

Weather Data ◽

Short Term ◽

Temperature Prediction ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

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WEATHER FORECAST PERCEPTIONS OF SAUDI ARABIAN CITIZENS: INITIAL STEPS TOWARDS BUILDING EXTREME WEATHER FORECASTING COMMUNICATION TECHNOLOGY

Weather Climate and Society ◽

10.1175/wcas-d-20-0152.1 ◽

2021 ◽

Author(s):

Abdulrahman Khamaj ◽

Amin G. Alhashim ◽

Vincent T. Ybarra ◽

Azham Hussain

Keyword(s):

Weather Forecasting ◽

Middle Eastern ◽

Protective Action ◽

Weather Forecast ◽

Weather Data ◽

Cross Sectional ◽

Daily Lives ◽

Weather Forecasts ◽

The Public ◽

Public Perspective

AbstractCommunicating weather forecasts from the public perspective is essential for meeting people’s needs and enhancing their overall experiences. Due to the lack of cited work on the public’s behavior and perception of weather data and delivery sources in Middle Eastern countries such as Saudi Arabia (KSA), this study employs a cross-sectional questionnaire to fill the gap and apply the Protective Action Decision Model to non-Western individuals. The questionnaire examined respondents’ opinions about 1) the importance of weather forecast accessibility, 2) crucial weather features, and 3) available features on existing smartphone weather applications (apps) in KSA. The results showed that nearly all participants reported that their decisions of daily lives and activities were highly dependent on weather forecasts. Most participants thought weather forecast features are necessary. Though the most commonly used source for weather forecasts in KSA was smartphone apps, many participants responded that these apps were lacking specific weather functionalities (e.g., giving weather alerts to their exact location). Regression analyses found that KSA individuals who do not believe that weather forecasts are important are predicted by 1) not wanting any new features added to weather applications and 2) that weather forecasts do not impact lives nor property. This study’s findings can guide governmental and private weather agencies in KSA and other Middle Eastern or developing countries to better understand how to meet and communicate people’s weather needs.

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Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/1616806 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Fang Zhao ◽

Ziyi Liang ◽

Qiyan Zhang ◽

Dewen Seng ◽

Xiyuan Chen

Keyword(s):

Neural Network ◽

Air Quality ◽

Short Term Memory ◽

Environmental Data ◽

Weather Data ◽

Short Term ◽

Concentration Data ◽

Data Set ◽

Term Memory ◽

Proposed Model

Accurate monitoring of air quality can no longer meet people’s needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time-series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long- and short-term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models.

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