Weather is King: The Importance of Numerical Weather Predictions in Energy Management

The roles weather events play in renewable energy sustainability can never be overestimated. It's no secret that the fulfilment of the EU's decarbonisation goals is heavily reliant on the continent's replacement of fossil fuels in the energy sector with Renewable Energy Sources (RES). However, the three major RES– solar, wind, and hydropower energy– can sometimes be vulnerable to certain weather conditions, and their utility depends on several climatic factors such as sunlight intensity, clouds, storms, wind speed and direction, water availability and flow rate.

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These weather events make RES susceptible to failure or inefficiency when not properly managed. However, with proper management comes a background of adequate anticipation. Therefore, to best forecast and handle these events, numerical weather prediction is necessary for maintaining ease and accuracy.

What is Numerical Weather Prediction (NWP)?

Numerical Weather Prediction is a method of weather forecasting that uses mathematical equations and employs mathematical, atmospheric, and oceanic models to predict the weather using current weather conditions as a datum.

How Does Numerical Weather Prediction Work?

NWP uses mathematical equations that, after being translated into computer codes, utilise governing equations–including conservation of mass, momentum, energy, and ideal gas laws; and numerical methods used to simplify these complex governing equations. Afterwards, with their initial and boundary conditions established, they're passed through and run over a domain (the domain is a geographical area).

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While the equations run over the domain, physical processes are likely to occur that are too intangible and complicated for the computer to understand. The cumulative effects of these latent physical processes are approximated in a process called parameterisation so that they can be clearly represented by governing equations and numerical methods.

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To achieve a high level of prediction accuracy, the forecasts are multiplied and compared either through a repetition of the processes or a collection of similar forecasts from separate units. Then, in a process known as ensemble forecasting, the resulting forecasts are plotted on a graph using infographics and observed. A low spread of axial lines indicates high accuracy, while a large spread indicates low confidence.

Any Relationship Between Numerical Weather Prediction and Energy Management?

The mention of energy management heralds a discussion on RES. For energy in Europe to be effectively managed, the focus on RES must be concentrated. NWP models happen to be an essential part of integrated Variable Renewable Energy (VRE).

Importance of Numerical Weather Prediction in Energy Management.

Grid operators and energy suppliers need accurate weather information and predictive indicators to manage energy and grid systems effectively. Equipped with the proper weather data, they can make decisions—such as supply control, surge management, and cost control. 

1. Numerical Weather Prediction Solves the Inaccuracy Limitation in Observed Weather Data

Accuracy achievement in forecast requires that each observation site have its solid past observations to create climatology. This is not always feasible with observed data. Observation sites do not run on simulation; thus, their accuracy relies on long-term observations if ensemble forecasting is to be conducted. 

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The data collected from a one-year observation, for instance, will not provide the result accuracy that a ten-year observation will yield. Continuous data collection is an expensive process not commensurate with forecast costs. Numerical weather prediction models erase this obstacle by providing simulated data of several atmospheric variables that are spatially well-adjusted and continuous.

2. Cost-effectiveness 

Traditional weather observation methods require long periods to build the sufficient confidence required for ensemble forecasting. The lengths of observation periods give rise to more forecast costs incurred through wages, equipment maintenance, error handling, and transportation.

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Numerical weather prediction helps save these additional costs by running on trained simulations that have been tested to yield results more accurately than traditional weather observation.

3. Productive Management of Interpolation Challenges

Interpolation is the estimation of a specific value or numeric variable of a certain function using data from other known variables of the same function. Interpolation is the fastest way to arrive at an unknown variable, but in the case of weather observation, this is hardly the case. 

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Traditional weather observations run for long periods, hence their heavy computed data. By their size and inconsistencies, these data can prove to be quite cumbersome for analysis. This encumbrance, in effect, results in more inconsistent results that are liable to have high negative impacts on energy management and sustainability. 

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However, weather forecaster solutions employing numerical weather prediction are programmed to handle these interpolation issues easily and precisely.

4. Effective Operation on Missing Data

Another obstacle plaguing traditional weather observation is likely data loss. Besides the inconsistency in data recorded, atmospheric data values can get missing during or after observation. The confusion that arises from this mishap can sometimes breed misinterpretation and system failure. With inadequate forecasting, energy suppliers and grid operators tend to lose control of power supply, energy surge, and power consumption owing to the absence of relevant information. 

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There's hardly a case of missing data with numerical weather prediction in the forecaster. The model works based on what it was trained for using current weather information. The simulation it runs affords it to preempt potential data loss since the results it comes out with are products of intertwined values. 

5. Provision of Location-Specific Data

Numerical weather prediction provides forecasters with valuable location-specific information. With their computerised algorithms, our forecasters with machine learning and hierarchical stochastic methods are trained to be as precise as possible. When input into specific equations, the numerical weather prediction uses specific values to give specific results.

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This special function goes a long way toward minimising errors that may be encountered in traditional weather observation methods. This can assist grid operators, and energy suppliers channel the necessary resources to the appropriate areas without misdirection. 

Conclusion

Energy can be managed in diverse ways, one of which is the employment of numerical weather prediction models when forecasting. Grid operators and energy suppliers require sufficient and accurate weather information to make prudent judgments and take the right steps in their mission to meet energy demands while keeping the atmosphere clean. With numerical weather prediction, grid operators and energy suppliers, with the cooperation of prosumers, can easily cut costs, save energy, engage in real-time maintenance, and even forecast weather events and derive the required information needed for fruitful grid management.

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