**Abstract**

The rising costs and undesirable environmental effects of traditional, nonrenewable energy sources have led to increased research regarding the viability of renewable energy sources. Wind has been the fastest-growing source of electricity generation in the world since the 1990s. One of the primary limiting constraints of wind energy is its reliability and there is no cost-effective mechanism for storing wind energy generated by a wind turbine, thus it must be quickly integrated into the electrical grid. The financial implications of wind forecasting are also of great consequence. A 1% error in forecasted wind speeds can result in a loss of \$12,000,000 during the facility's life time. As more wind power is incorporated into electricity markets, the capacity to correctly and precisely estimate wind speeds has become increasingly vital. Hence, the importance of this chapter by addressing the different divisions related to wind speed prediction into two overall groups. The first group is based upon analysis of historical time series of wind energy forecasting explanatory variables which are generated from a meteorological model of wind dynamics and the second uses forecasted values from a numerical weather prediction (NWP) model as an input to utilize a statistical approach to anticipate energy predication.

**Keywords:** wind energy, forecasting models, short-term time prediction, long-term forecasting, physical forecasting, statistical wind forecasting

### **1. Introduction**

More than ever, the world has to work together to find renewable energy solutions to combat the Climate Crisis. Since 2015, all UN Member States have committed to ensuring that everyone has access to cheap, dependable, sustainable, and contemporary clean energy by 2030. Clean energy is derived from renewable natural resources such as the sun, wind, tides and waves, and geothermal [1].

Renewable energy sources, such as solar and wind energy, are more volatile than traditional energy sources since they are weather-dependent. As many countries throughout the world expand their renewable energy supply [2, 3], it is critical to ensure that these clean energy sources offer a consistent supply while replacing fossil fuel-based energy sources. The renewable energy applications range from large-scale and off-grid electricity generating (for rural and remote areas) [1] to heating/cooling systems and transportation.

Wind energy is one of the most widely used renewable energy sources, accounting for 4.8 percent of global electricity production in 2018 [4, 5] and 15 percent of Europe's electricity consumption in 2019 [6]. The mechanical power of the wind is

used to power turbines that generate electricity, which generates wind energy. Because wind has a fluctuating intensity over time and might stop blowing at any time, electricity generated by this source is frequently coupled with other power sources to improve reliability and stability.

Wind energy is one of the RES with the lowest electricity production costs and the largest available resource. As a result, a growing number of countries are realizing that wind power offers a great future power generation opportunity.

By dealing with the intermittence characteristic of wind, forecasting methods can improve wind position. Although wind energy cannot currently be dispatched, the financial impacts of wind can be greatly decreased if wind energy can be scheduled using precise wind predictions. As a result, improving wind power output and developing a wind speed forecasting tool has a huge economic and technical impact on the system, **Figure 1** detailed classification of deterministic wind speed and power forecasting.

A number of institutes and organizations with extensive experience in the subject have dedicated numerous studies to the advancement of wind forecasting techniques. Models like WPMS, WPPT, Prediktor, ARMINES, Previento, and others have been developed and deployed in wind farms all around the world. Physical, statistical, and hybrid methodologies were used to develop these models, **Table 1** presents a list of wind power software prediction models developed internationally.

In general, wind forecasting is mostly concerned with the immediate-short-term of minutes to hours to commonly up to 1 day and the long-term of up to 2 days. WPMS, as an example of immediate-short-term models, currently predicts wind generation

#### **Figure 1.**

*Detailed classification of deterministic wind speed and power forecasting. Image taken from: ref. [7]. Creative Commons Attribution 4.0 International (CC BY 4.0).*


**Table 1.**

*Presents wind power software models prediction internationally.*

for over 95 percent of Germany's territory. Reference [5] discusses immediate short-term wind forecasting models. In addition, various models for short-term wind forecasting have been created, such as Predictor, Zephyr, AWPPS, and Ewind, which are all based on high precision numerical weather prediction (NWP) [6, 8]. Previento, which employs a hybrid technique, can anticipate wind for up to 48 hours. References [9, 10] include more studies on long-term forecasting models.

Variations in energy production (induced by variations in wind speed) will become more noticeable on the electrical system as the penetration of wind power generation grows (in terms of the overall energy mix). To avoid balancing concerns, Transmission System Operators (TSOs) operating to balance supply and demand on regional or

national grid systems will need to foresee and manage this unpredictability. The moment at which this is necessary varies by system, although it has been noted that it becomes critical when wind energy penetration reaches roughly 5% of installed capacity.

As wind energy's penetration into individual networks grows, it will be important to make wind farms look more like conventional plants, necessitating the ability to estimate how much energy will be produced over short to medium periods (1 hour to 7 days). Operators, managers, and TSOs commonly anticipate the output from their wind farms in European nations where there is already a substantial level of penetration, such as Spain, Germany, and Denmark. These estimates are used to plan the operations of other factories and for trading.

As the amount of installed capacity develops, forecasting wind energy generation will become more important. The wind industry must expect to do everything possible to enable TSOs to use wind energy to its full potential, which necessitates reliable aggregated output estimates from wind farms.

At the same time as improving the predictability of wind energy plant production through better forecasting tools, it is important to be aware of the true behavior of conventional plants. All of the different energy forms must be considered on an equal level in order to produce the best mix of plants and technologies. As a result, a comprehensive statistical analysis of renewable and conventional plants is critical. This task should be viewed as a critical component of a wind energy development plan, and it should be approached from a comprehensive power system standpoint.

Electricity producers, which include corporations that run wind farms, sell predetermined amounts of energy (measured in kWh) to regional or national energy companies (in the case of wind energy). Because the grid is intended to provide a constant supply of electricity, governments may punish energy producers with large fines if there are power outages.

Energy trade businesses play a critical role in assessing the risk of energy transaction shortfalls by assisting in the forecasting of expected energy production (especially in the case of wind, as a non-steady energy source). Energy dealers, on behalf of energy producers, forecast energy production (in our case, wind energy) using two scenarios:


In this regard, accurate energy output forecasting is critical to the financial performance of wind farms (i.e. wind energy producers).
