- Author: Mahajabin Ahmed
- LG-ID: 27199320190101908
- Mail Address
- Publication No: 397458
- Type: Original | Open Access
- Category: Engineering & Technology
- Received: July 13, 2023
- Accepted: September 11, 2023
- Published: November 29, 2024
Sections
Abstract
This study is centered on the climatic and energy variables aimed at capturing their non-linear relationship; using the climatic variables which include mean air temperature, wind speed, average sun hours, and rainfall, with energy variables which include the total demand and maximum generation. Secondary data research conducted with 64 observations to see correlation through regression, ANOVA, etc. Total energy demand is found to change with temperature increase thus implying the existence and effect of seasonal changes in energy demand, for rainfall showed positive relationship with wind energy demand and speed. Basic summary statistics showed that there is a variability and seasonality in the data with mean air temperature between 20.0 and 26.5 degree Celsius while the total demand fluctuated between 5000 MW and 6200 MW. This therefore calls for incorporation of climate variability whilst demand forecasting in order to improve energy grids as well as renewable energy storage. Strategic limitations, including reducing the size of the dataset and not including other variables of interest, are discussed, as are emerging prospects, including more sophisticated algorithms and real-time climate surveillance services. The findings of this study can benefit policymakers and researchers as they will be preparing a framework upon which to develop adaptive energy systems for various climates.
Keywords
Nonlinear dynamics, Chaos theory, Predictive models, Climate systems, Energy systems
Introduction
1.1 Background
Nonlinear dynamics are critically important considerations when studying the interconnections between energy systems and climatic parameters. The research shows that these systems follow a in which temperature, wind speed, and rainfall interfere with energy demand and generation. For instance, fluctuations in the price of air optionally affect the requirements for heating or cooling plants that affect energy use. Other sources of energy include the solar and wind energy, the development of which are also highly dependent on climatic conditions thus a great need for assessing the various linkages (Lenton, 2011).
Catastrophe theory, as a subset of nonlinear dynamics, provides methods of studying seemingly stochastic yet deterministic processes in numerous large-scale networks. It has been widely applied to discover latent connections between the variables in energy systems for increasing the predictive efficiency (Fernández-Díaz, 2023). Linear models cannot analyze such dependencies because they depict consecutive periods as unrelated, whereas nonlinear models allow identifying how variations in climatic characteristics affect energies’ demand and generation.
The increasing trends in climate variability prompted by global warming call for improved comprehension of these patterns. A recent IPCC’s Sixth Assessment portrays climate patterns as becoming increasingly uncertain, meaning that more complex techniques integrating climate data with energy system modelling are necessary (Ives et al., 2021). Chaos theory and other statistical problems are important to reduce the difference between the climatic variance and energy dependence (Legg, 2021).
1.2 Objectives
This study aims to determine nonlinear relations between average mean temperature, average wind speed , daily sunlight hours, monthly rainfall, total energy consumption, and maximum generation. As anticipated from the 64 observations, secondary data used in this research examines climate factors and energy demand and generation patterns. To this end, regression and ANOVA tests are employed in the analysis to test these relations. Besides, the paper evaluates effectiveness of patterns and dependencies in these models to improve the results of energy management procedures.
1.3 Scope
The empirical analysis of this study includes the examination of secondary data set of 64 observations from climatic and energy variables The climate parameters that are contained in the dataset are mean air temperature, average wind speed, daily sun hours, monthly rainfall, total energy demand and maximum generation. Regression and ANOVA, which are analyzed using SPSS to know the nonlinear dynamics. Although the analysis is based only on this dataset, the proposed approaches can be used to analyze other climate parameters and discuss their relevance to the energy systems’ modeling and prognosis as well as the development of management solutions.
Literature Review
2.1 Chaos Theory in the development of the Predictive models
Chaos theory has greatly improved comprehension of programs just whereby non-linearity together with unpredictability holds the key. In his subsequent work on deterministic nonperiodic flow Lorenz managed to formalize the idea of how slight variations in initial state may produce widely different results; that lead to the concept of the “butterfly effect” (Lorenz, 1963). This has been taken to energy systems whereby small changes in climate conditions can have a very profound impact on energy consumers and renewable energy producers. Since these systems are characteristically stochastic, they may be well suited for model predictive models under chaos theory.
New trends in energy forecasting have incorporated chaos theory particularly with renewable energy systems. For example, Ramadevi and Bingi, (2022) presented a literature analysis of chaotic time series forecasting techniques with the use of machine learning perspectives where nonlinear interactions of energy forecasts were expounded. Chaos theory models have also applied in scheduling multiple renewable resources, for example wind and solar energy under uncertain situations (Du et al., 2019). These applications show that the application of chaos theory is relevant and pertinent in an effort to understand the dynamics of energy systems in order to optimize energy usage.
2.2 Interactions between Climatic and Energy Parameters
Meteorological parameters like temperature, rainfall and wind speed of climate have straight impact on the energy consumption and Renewable energy generation. For instance, note that with higher temperatures energy for cooling increases, and with low temperatures energy for heating also increases, which heavily impacts on electricity usage (Pérez-Lombard et al., 2008). Likewise, domestic wind and solar energy generation directly relates to local weather conditions: Wind energy hugely relies on wind speed while solar irradiance volatility significantly affects renewable energy output.
Climate change has led to relative relationships between climatic factors and energetic systems becoming more intricate over time. , and Neupane et al. (2022) analyzed climate change effects on crop yields and food security, which can be also compared to climate-dependent energy sources such as hydropower and wind energy. These results view the use of climate models in energy management as important. In addition, Du et al. (2019) provided understanding on performance of wind and solar integrated multi renewals investigating various issues and beneficial aspects related to generation of energy under fluctuating natural conditions. These studies stress the timely inclusion of climatic data into the energy management trend forecasting models.
2.3 Statistical Methods for Analyzing Complex Systems
Regression and high-order time series analysis are well established in the analysis of systems which incorporate climatic and energy parameters. These methods assist in getting the correlation between the independent variables like; wind; speed, temperature, etc., and dependent variables like energy consumption, etc. For example, Fan and Hyndman (2011) used a semi-parametric additive model for the short-term load forecast where more precise approach was used in view of interaction unique in the model. Such regression techniques also continue to be important for modeling energy use patterns over time together with the impacts of climatic fluctuations on energy systems.
Linear approaches are still relevant and widely used Cynthia Glenwood. Other approaches have been blended with statistical models to work well on the nonlinear systems to the nonlinearities. Ramadevi and Bingi (2022) on the use of machine learning method to chaotic time series particularly on renewable energy system since nonlinearity characterizes the system. Similarly, Du et al., (2019) has shown that the above conventional statistical tools can help in developing relations to model interactively the wind and solar energy systems as part of systems with uncertainty towards resources. All these methods when used in conjunction with chaos theory result in accurate forecast and thus control of energy systems particularly under changing climate conditions.
3. Methodology
3.1 Dataset Description
In the current study, the dataset involves 64 observations incorporating variables of climate and energy systems. The climatic factors include temperature (°C), wind speed (m/s), daily sunshine (hrs.) and precipitation (mm). Sales control parameters comprise of energy-related factors such as total demand factor (MW) and maximum generation factor (MW). These were selected because they define the relationship between climatic changes and energy systems.
Out of exploration, preliminary data quality check did not show any values that were missing from the database to be investigated. Also, no data pre-processing was done because in some cases the raw values adequately describe the relations needed for the analysis. Thus, this dataset corresponds to prior investigations that employ analogous variables to analyze the effect of climate on energy (Pérez-Lombard et al., 2008; Neupane et al., 2022).
3.2 Data Analysis Techniques
The analysis involves two primary statistical techniques: have kept regression analysis and ANOVA are out of the realm of undergraduate mathematical statistics. To establish the correlation between mean air temperature / rainfall and wind speed, total demand and maximum generation regression models were employed. This approach has been more frequently applied to estimate the interactions between energy systems and climate since it can capture linear and nonlinear relationships on mediocre to high quality (Fan and Hyndman, 2011).
To analyze variation within the climatic and energy variables, and season as well as group differences, ANOVA was used. This technique works hand in hand with regression as it controls the impact of categorical factors on measure variables.
3.3 Statistical Tools
All analyses of the study were processed using SPSS software for regression analysis, ANOVA, and descriptive statistics. Due to its simple graphical user interface and powerful statistical features, it can be used for studying nonlinear dependencies in complex entities.
4. Results
4.1 Regression analysis
The present study aimed at analyzing climate factors, hence the dependent variables; air temperatures and rainfall and energy factors hence the independent variables; total energy demand, the maximum generation and wind speed. To this end, two models were created.
Model 1: Mean Air Temperature as the dependent Variable
The regression model tested on the mean air temperature had a positive significant relationship with the total energy demanded (p = 0.004). Thus, the results indicate that a larger energy use is linked to rising air temperatures – which might be attributed to demand for cooling during the summer. Surprisingly, there was no correlation between wind speed and max generation with p = 0.234 and p = 0.454 respectively. The model got an R-squared of 0.493, meaning that about 49.3% in variability in air temperature was accounted for by the predictors. These results may be contrasted with other such works as Pérez-Lombard et al (2008) on Nonlinear temperature demands for energy.
Variables Entered/Removeda | |||
Model | Variables Entered | Variables Removed | Method |
1 | average_wind_speedms, total_demandmw, max_generationmwb | . | Enter |
a. Dependent Variable: mean_air_temperatureC | |||
b. All requested variables entered. | |||
Model Summary | ||||
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
1 | .702a | .493 | .468 | 3.2895 |
a. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||
ANOVAa | ||||||
Model | Sum of Squares | df | Mean Square | F | Sig. | |
1 | Regression | 631.462 | 3 | 210.487 | 19.452 | .000b |
Residual | 649.246 | 60 | 10.821 | |||
Total | 1280.709 | 63 | ||||
a. Dependent Variable: mean_air_temperatureC | ||||||
b. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||||
Coefficientsa | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | -6.360 | 4.267 | -1.491 | .141 | |
total_demandmw | 8.571E-5 | .000 | .891 | 2.962 | .004 | |
max_generationmw | -2.154E-5 | .000 | -.228 | -.754 | .454 | |
average_wind_speedms | -.410 | .341 | -.113 | -1.202 | .234 | |
a. Dependent Variable: mean_air_temperatureC | ||||||
Figure: Model 1
Model 2: Average monthly rainfall as dependent variable
Significantly, just like for the mean rainfall, there was an increased correlation between the wind speed and average rainfall (p=0.018). However, the total energy demand showed a negative relationship with the average rainfall levels at a statistically significant level of 0.005. From such results, this paper is in a position to conclude that increased rainfall is accompanied by increased wind intensity but lower energy consumption, perhaps because of the lower cooling demands. Max generation though, did not have statistical significance (0.123). The model obtained through the analysis had R squared of 0.349 meaning that the independent variables accounted for 34.9% of variation in rainfall. These outcomes present climatic aspect in contributing for load demand and exhibit how such relation could be multifaceted (Fan and Hyndman, 2011; Neupane et al., 2022).
Variables Entered/Removeda | |||
Model | Variables Entered | Variables Removed | Method |
1 | average_wind_speedms, total_demandmw, max_generationmwb | . | Enter |
a. Dependent Variable: average_monthly_rainfallmm | |||
b. All requested variables entered. | |||
Model Summary | ||||
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
1 | .591a | .349 | .317 | 47.8999 |
a. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||
ANOVAa | ||||||
Model | Sum of Squares | df | Mean Square | F | Sig. | |
1 | Regression | 73826.293 | 3 | 24608.764 | 10.726 | .000b |
Residual | 137664.164 | 60 | 2294.403 | |||
Total | 211490.457 | 63 | ||||
a. Dependent Variable: average_monthly_rainfallmm | ||||||
b. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||||
Coefficientsa | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 204.814 | 62.133 | 3.296 | .002 | |
total_demandmw | -.001 | .000 | -.990 | -2.903 | .005 | |
max_generationmw | .001 | .000 | .537 | 1.565 | .123 | |
average_wind_speedms | 12.057 | 4.968 | .258 | 2.427 | .018 | |
a. Dependent Variable: average_monthly_rainfallmm | ||||||
Figure: Model 2
4.2 ANOVA Findings
To examine the variance of climatic or energy variables across the groups, analysis of variance (ANOVA) was used. For mean air temperature, the sum of squares total was 1280.709 (degree of freedom = 63) and the F-statistic explained a significant amount of variability (p < 0.001), were assigned to the predictors. This goes a long way in illustrating that total demand for energy for instance has rather large effects on air temperature.
Likewise, for average monthly rainfall the total sum of squares was 211490.457 (repeated measures ANOVA test, df = 63) with significant group level difference (p < 0.001). This also shows the effect of such factors as the wind speed and energy demand on rainfall distribution, which supports other studies on relationship between climatic and energy factors (Du et al., 2019). These ideas suggest that statistical models should be employed in analyzing these intricate links.
ANOVA | ||||||
Sum of Squares | df | Mean Square | F | Sig. | ||
mean_air_temperatureC | Between Groups | 1280.709 | 63 | 20.329 | . | . |
Within Groups | .000 | 0 | . | |||
Total | 1280.709 | 63 | ||||
average_wind_speedms | Between Groups | 97.190 | 63 | 1.543 | . | . |
Within Groups | .000 | 0 | . | |||
Total | 97.190 | 63 | ||||
average_daily_sun_hours | Between Groups | 222.355 | 63 | 3.529 | . | . |
Within Groups | .000 | 0 | . | |||
Total | 222.355 | 63 | ||||
average_monthly_rainfallmm | Between Groups | 211490.458 | 63 | 3356.991 | . | . |
Within Groups | .000 | 0 | . | |||
Total | 211490.458 | 63 | ||||
Figure: ANOVA
4.3 Descriptive Statistics
The mean, standard deviations, correlation coefficients, standard errors of the mean and range display basic characteristics of the variables included in the dataset. Mean AIR TEMPERATURE (°C) = 23.25 St.Dev = 1.75 The observed mean ranging usually in the range of 20.0-26.5 °C. Average Wind Speed (m/s) has the largest mean value of 5.5 (± 0.34) and standard deviation of 1 with minimum value of 5.0 and maximum value of 6.0. Average Daily Sun Hours is also normal, with a mean of 8.2 (Standard deviation of 1.16) and varies between 6.0 and 10.4. Average Monthly Rainfall (mm) is 100.0, with the deviation of 11.95, range of 80.0 to 120.0. Total Demand (MW) is normally distributed with a Mean = 5650.0 , Standard Deviation = 518.84, Range = 5000.0 – 6200.0 whereas Max Generation (MW) is also normally distributed with Mean = 5775.0, Standard Deviation = 612.79 and Range = 5500.0 –7750.0. These metrics represent variation and seasonality important to regression and time series analysis.
Statistic | Mean Air Temperature (°C) | Average Wind Speed (m/s) | Average Daily Sun Hours | Average Monthly Rainfall (mm) | Total Demand (MW) | Max Generation (MW) |
count | 64.00 | 64.00 | 64.00 | 64.00 | 64.00 | 64.00 |
mean | 22.62 | 5.49 | 8.10 | 99.69 | 6575.00 | 6578.12 |
std | 1.76 | 0.34 | 1.41 | 14.14 | 1055.60 | 727.68 |
min | 20.00 | 5.00 | 6.00 | 80.00 | 5000.00 | 5500.00 |
25% | 21.00 | 5.20 | 6.80 | 90.00 | 5600.00 | 6000.00 |
50% | 22.50 | 5.40 | 8.00 | 100.00 | 6500.00 | 6500.00 |
75% | 24.00 | 5.80 | 9.20 | 110.00 | 7400.00 | 7250.00 |
max | 25.50 | 6.00 | 10.40 | 120.00 | 8300.00 | 7750.00 |
Discussion
5.1 Interpretation of Results
The analysis of-climate and energy factors showed correlation coefficients that were significant and provided the study with insights into the nature of change of climate and the corresponding changes in energy usage. The presence of direct dependency between mean air temperature and total energy demand points out to the seasonality of temperature, and hence its importance for energy consumption, especially for cooling systems. This result is in harmony with other works like Pérez-Lombard et al. (2008), who emphasized the notion that the temperature is among the major driving forces of electricity usage. Knowledge of this connection may aid energy planning by allowing the planners to determine when electricity is most in demand and to allocate their resources well.
Likewise, the correlation was evident when rainfall has an impact on wind speed – it also proved that environmental factors work hand in hand. Daily variation in wind speed in rainy months could also affect the wind energy production and offer prospects of incorporating wind power such as windmills in particular districts. In similar regard, we find some evidence of negative relationship between rainfall and total energy demand indicating that energy consumption is lower in the wet months since cooling intensity is lower. This is in consonance with findings made in models of fluctuating energy demand patterns over a seasonally changing calendar year as proposed by Fan and Hyndman (2011).
The coefficient of determination is moderate for both mean air temperature and average rainfall: respectively 0.493 and 0.349. Despite this, it is clear that other factors such as humidity, socio-economic factors or any extreme weather type may increase the model’s accuracy. These results demonstrate the real challenge of analyzing energy systems characterized by non-linear interdependencies between climate and energy factors.
This brings implication for energy management since the research findings demonstrate an activity-based energy pattern. For example, utilities should benefit from real time climate data in terms of planning energy generation and demand patterns plus coming up with coping strategies in the event of unfavorable climates. Further, perceptions about climate and energy systems can inform investments towards a sustainable renewable energy systems transition.
5.2 Limitations
However, this research has its own limitations as follows: Altogether the data sample includes only 64 responses, which constrains the study’s range and conclusions. In some cases, the sources of secondary data may be biased, this is incompatibility of both data collection methods and the reporting style. Also, restrictions concerning the brief set of variables including temperature, wind speed and rainfall ignore other factors including humidity, variability of solar radiation and socio-economic conditions which are known to influence the energy usage pattern.
The third limitation follows from the use of linear regression that, although useful to identify general relationships, disguises a nonlinear nature of often studied climate-energy systems. It seems then that only more sophisticated models, like perhaps neural nets or a statistical/chaos theory approach, are capable of capturing such nonlinear interactions. Furthermore, the study fails to consider the possibility of dynamic climatic or energy structure transformation in future that also affects the outlook.
5.3 Future Directions
For these reasons, further investigation should include other variables, including humidity, solar radiation, and socio-economic factors to improve the improvement of predictive models. Further research would be useful not only to increase observation periods, even if it means collecting data from different geographical locations, to enhance generalizability of outcomes.
Further use of sophisticated modeling approaches, including machine learning models and the integration of statistical models with chaos theory may enable additional understanding of complex relationships between climatic and energy factors. Such approaches could improve the accurate prediction as far as the renewable energy systems which are loaded with climatic variabilities are concerned.
Other possible subsequent investigations can be focused on such topics as dynamic methods of climate tracking to promote efficient energy management. These systems would have boosted capability in terms of accurate prediction of demand as well as proper utilization of renewable energies by the energy providers. Both policymakers and researchers would greatly benefit from synergy in integrating research results and implementation approaches promoting sustainable and climate-resilient energy systems.
6. Conclusion
This work poses essential questions that help evaluate the nature of connections between climatic factors and energy systems, with an emphasis on chaotic behavior. Multiple predictors of mean air temperature and rainfall were established using regression analysis with considerable contributions of total energy demand and wind speed respectively. These results stress the need for the consideration of climatic variation in energy demand forecasting to enhance its reliability and catastrophe preparedness.
The fact that total energy requirement increased as temperature increased also points to the need for dynamic energy systems responsive to customer consumption patterns. Likewise, the relationship between rainfall and wind speed points out the need to balance the utilization of renewable power, particularly wind electricity with energy infrastructure. These findings are consistent with the prior literature, for instance Pérez-Lombard et al., (2008) which pointed at the centrality of climatic variables in influencing energy usage.
On the level of applied knowledge, thus, the study avails policymakers and energy planners with realizable new knowledge regarding the climate-energy interface. Therefore, the research implies that energy systems should be developed in order to factor the variability of demand in respect to climatic conditions. Also, enhanced climate prediction, and complex computational models might help in improving utilization of available capital to decrease dependency on fossil fuel and increase capacity factor of the grid .
These contributions present significant added value to the field of nonlinear dynamics, especially in the management of energy systems. However, some of the limitation include the sample size and the exclusion of more variables opens doors for more research. Future research can consequently improve the analysis’ scope and use more complex methods to offer a more nuanced approach to addressing the climate variability issues presented herein.
It is now incumbent on governments and social scientists to translate these insights into applications, in order to develop reliable and resilient systems given the ongoing processes of climate change. This work lays the groundwork for future examinations of complex interactions of climate and energy systems and helps build resiliency against growing climate risk.
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Appendix
Time series
Model Description | ||
Model Name | MOD_1 | |
Series or Sequence | 1 | mean_air_temperatureC |
2 | average_wind_speedms | |
3 | average_daily_sun_hours | |
4 | average_monthly_rainfallmm | |
5 | max_generationmw | |
6 | total_demandmw | |
Transformation | None | |
Non-Seasonal Differencing | 0 | |
Seasonal Differencing | 0 | |
Length of Seasonal Period | No periodicity | |
Horizontal Axis Labels | month | |
Intervention Onsets | None | |
For Each Observation | Values not joined | |
Applying the model specifications from MOD_1 | ||
Case Processing Summary | ||||||||
| mean_air_temperatureC | average_wind_speedms | average_daily_sun_hours | average_monthly_rainfallmm | max_generationmw | total_demandmw |
| |
Series or Sequence Length | 64 | 64 | 64 | 64 | 64 | 64 |
| |
Number of Missing Values in the Plot | User-Missing | 0 | 0 | 0 | 0 | 0 | 0 |
|
System-Missing | 0 | 0 | 0 | 0 | 0 | 0 |
| |
Regression
Dependent : Temp
Variables Entered/Removeda | |||
Model | Variables Entered | Variables Removed | Method |
1 | average_wind_speedms, total_demandmw, max_generationmwb | . | Enter |
a. Dependent Variable: mean_air_temperatureC | |||
b. All requested variables entered. | |||
Model Summary | ||||
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
1 | .702a | .493 | .468 | 3.2895 |
a. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||
ANOVAa | ||||||
Model | Sum of Squares | df | Mean Square | F | Sig. | |
1 | Regression | 631.462 | 3 | 210.487 | 19.452 | .000b |
Residual | 649.246 | 60 | 10.821 |
|
| |
Total | 1280.709 | 63 |
|
|
| |
a. Dependent Variable: mean_air_temperatureC | ||||||
b. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||||
Coefficientsa | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | -6.360 | 4.267 |
| -1.491 | .141 |
total_demandmw | 8.571E-5 | .000 | .891 | 2.962 | .004 | |
max_generationmw | -2.154E-5 | .000 | -.228 | -.754 | .454 | |
average_wind_speedms | -.410 | .341 | -.113 | -1.202 | .234 | |
a. Dependent Variable: mean_air_temperatureC | ||||||
Regression (Dependent Rainfall)
Variables Entered/Removeda | |||
Model | Variables Entered | Variables Removed | Method |
1 | average_wind_speedms, total_demandmw, max_generationmwb | . | Enter |
a. Dependent Variable: average_monthly_rainfallmm | |||
b. All requested variables entered. | |||
Model Summary | ||||
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
1 | .591a | .349 | .317 | 47.8999 |
a. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||
ANOVAa | ||||||
Model | Sum of Squares | df | Mean Square | F | Sig. | |
1 | Regression | 73826.293 | 3 | 24608.764 | 10.726 | .000b |
Residual | 137664.164 | 60 | 2294.403 |
|
| |
Total | 211490.457 | 63 |
|
|
| |
a. Dependent Variable: average_monthly_rainfallmm | ||||||
b. Predictors: (Constant), average_wind_speedms, total_demandmw, max_generationmw | ||||||
Coefficientsa | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 204.814 | 62.133 |
| 3.296 | .002 |
total_demandmw | -.001 | .000 | -.990 | -2.903 | .005 | |
max_generationmw | .001 | .000 | .537 | 1.565 | .123 | |
average_wind_speedms | 12.057 | 4.968 | .258 | 2.427 | .018 | |
a. Dependent Variable: average_monthly_rainfallmm | ||||||
ANOVA
ANOVA | ||||||
| Sum of Squares | df | Mean Square | F | Sig. | |
mean_air_temperatureC | Between Groups | 1280.709 | 63 | 20.329 | . | . |
Within Groups | .000 | 0 | . |
|
| |
Total | 1280.709 | 63 |
|
|
| |
average_wind_speedms | Between Groups | 97.190 | 63 | 1.543 | . | . |
Within Groups | .000 | 0 | . |
|
| |
Total | 97.190 | 63 |
|
|
| |
average_daily_sun_hours | Between Groups | 222.355 | 63 | 3.529 | . | . |
Within Groups | .000 | 0 | . |
|
| |
Total | 222.355 | 63 |
|
|
| |
average_monthly_rainfallmm | Between Groups | 211490.458 | 63 | 3356.991 | . | . |
Within Groups | .000 | 0 | . |
|
| |
Total | 211490.458 | 63 |
|
|
| |
Peer Review Acknowledgment
This research underwent a thorough evaluation by the peer review committee of the Lilac Institute of Technology (LIT), a division of Lilac Education. The committee’s critical insights and scholarly expertise significantly contributed to this work’s academic quality and analytical depth. We extend our gratitude to LIT for their commitment to maintaining high standards of academic rigor and excellence in research.
Cite the article
Ahmed, M. (2024). Exploring nonlinear dynamics in complex systems: Application of chaos theory to predictive models in climate and energy systems. Lilac Education Press (Publication No. 397458). https://press.lilaceducation.com/exploring-nonlinear-dynamics-in-complex-systems