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The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm.

1. Alien invasive plants possessing attractive flowers can affect the interactions between native plants and their pollinators. The few studies conducted so far have reported positive, negative and neutral effects of the presence of an invasive species on the pollinator visitation rates and seed output of native species. However, the role played by the density of the alien species has been seldom explored. 2. While high densities of the invasive species can negatively effect the pollinator visitation rates and seed output of the native species, due to sequestration of pollinators by the invasive species, at lower densities the invasive can attract pollinators that otherwise would not visit patches of native species, positively affecting their reproduction. 3. Using observations and pollinator exclusions at a site at 2800 m altitude in the central Chilean Andes, we show that the alien herb Taraxacum officinale (Asteraceae), possessing showy floral capitula, shares pollinators to a high degree (> 90%) with two co-occurring native Asteraceae: Hypochaeris thrincioides and Perezia carthamoides. Pollinator exclusion indicated that both natives are highly dependent on insect visitation to produce seeds. 4. Using manipulative supplemental hand-pollination and experiments, where we varied the density of T. officinale, we determined that seed output in H. thrincioides and P. carthamoides is pollen-limited and that its magnitude is not affected by the presence of the exotic. Further, while the presence of one individual of T. officinale around focal native individuals showed neutral or facilitative effects on pollinator service or seed output in the two native species, the presence of five individuals of T. officinale negatively affected these reproductive variables. 5. Synthesis. Our results suggest that the effects of the presence of alien invasive plant species with attractive flowers vary with their density. This demonstrates the need to experimentally test for the potential impacts of introduced species at different densities before prematurely coming to conclusions regarding their assumed negative or positive effects in native ecosystems.

A high-quality set of typical scenarios is significant for power grid planning. Existing construction methods for typical scenarios do not account for the spatiotemporal correlations among renewable energy plant outputs, failing to adequately reflect the distribution characteristics of original scenarios. To address the issues mentioned above, this paper proposes a construction method for typical scenarios considering spatiotemporal correlations, providing high-quality typical scenarios for power grid planning. Firstly, a symmetric spatial correlation matrix and a temporal autocorrelation matrix are defined, achieving quantitative representation of spatiotemporal correlations. Then, distributional differences between typical and original scenarios are quantified from multiple dimensions, and a scenario reduction model considering spatiotemporal correlations is established. Finally, the genetic algorithm is improved by incorporating adaptive parameter adjustment and an elitism strategy, which can efficiently obtain high-quality typical scenarios. A case study involving five wind farms and fourteen photovoltaic plants in Belgium is presented. The rate of error between spatial correlation matrices of original and typical scenario sets is lower than 2.6%, and the rate of error between temporal autocorrelations is lower than 2.8%. Simulation results demonstrate that typical scenarios can capture the spatiotemporal correlations of original scenarios.

Over the past decade, a steady tendency towards the increasing use of renewable energy sources in the fuel and energy complex of separate countries and regions is observed. Countries that have large hydrocarbon reserves such as the Republic of Kazakhstan are no exception. The main challenges of using renewable energy sources in these countries are related not only to their spatial and temporal variety, but also to the high cost of produced energycomparing to the traditional energy B sources based on hydrocarbon fuel. The solution to the mentioned problems can be achieved by optimizing the use of renewable energy resources, which requires a comprehensive analysis of the territory in terms of physical-geographic conditions and economic factors. The article offers methodological approaches and the results from a spatial analysis of the energy supply potential to consumers in the Republic of Kazakhstan using the cartography tools. It takes into account not only the distribution of resource potential, but also the environmental factors (specially protected natural areas, water conservation zone, etc.) limiting the installation of power plants based on RES, and the energy consumption schedule. Economic factors include the performance of typical power plants based on RES, as well as the present value of potentially generated energy.

This study presents performance limitations in the control of single-input linear time-invariant plants when controlled over a fading channel. The authors consider two architectures where the controller is co-located with the sensors. In the first architecture, the authors assume that delayed controller to actuator channel state information is available at the controller. In the second architecture, the authors relax that assumption and thus no channel-state-information is exploited at the controller. The authors’ main result is a closed form expression for the minimal stationary plant output variance, which is achievable in each scenario, as an explicit function of channel statistics and plant characteristics. To derive our results, the authors first show that there exists an equivalence, in a second-order moment sense, between communication over a single fading channel and communication over an additive white noise channel subject to a stationary signal-to-noise ratio (SNR) constraint. Such equivalence is then exploited to state conditions for stabilisation, and to derive explicit performance limitations, as simple corollaries of known results in the literature on networked control subject to SNR constraints. Numerical examples are included to illustrate our findings.

The development of wind-and solar-generating capacity is growing rapidly around the world as policy makers pursue various energy policy objectives. This paper will describe the challenges in integrating wind and solar generation, the lessons learned, and recommended strategies from both operating experience and integration studies. Case studies on the experience with wind and solar integration in China, Germany, and Spain are also included in this paper. The paper is organized as follows. First section summarizes worldwide wind and solar development, the challenges in integrating wind and solar generation, and some of the lessons learned from studies designed to evaluate the impact of higher levels of wind and solar generation and also from the operational experience in some countries with larger amounts of renewable energy. The second section summarizes some of the solutions for incorporating higher levels of wind and solar capacity into short-term system operations. This section also explains basic methodologies to implement system operations studies to understand the impacts of variability in system operation. The third section explains the contribution of variable renewables to long-term supply adequacy-commonly called 'firm' power-and the relationship of this to long-term reserves; it also explores how these issues can be incorporated into long-term planning or adequacy assessments. Overall, the variability of wind power generation adds to the variability on the grid in most time scales, and a key question that wind integration studies must address is whether there is enough existing capability on the grid to manage that increased variability, or whether new sources, such as new generation or increased levels of demand response, must be added to manage that variability.

Soil nutrient availability directly enhances vegetative growth, flowering, and fruiting in alpine ecosystems. However, the impacts of nutrient addition on pollinator visitation, which could affect seed output indirectly, are unknown. In a nutrient addition experiment, we tested the hypothesis that seed output in the insect-pollinated, self-incompatible shrub, Chuquiraga oppositifolia (Asteraceae) of the Andes of central Chile, is enhanced by soil nitrogen (N) availability. We aimed to monitor total shrub floral display, size of flower heads (capitula), pollinator visitation patterns, and seed output during three growing seasons on control and N addition shrubs. N addition did not augment floral display, size of capitula, pollinator visitation, or seed output during the first growing season. Seed mass and viability were 25-40% lower in fertilised shrubs. During the second growing season only 33% of the N addition shrubs flowered compared to 71% of controls, and a significant (50%) enhancement in vegetative growth occurred in fertilised shrubs. During the third growing season, floral display in N addition shrubs was more than double that of controls, received more than twice the number of insect pollinator visits, and seed output was three- to four-fold higher compared to controls. A significant (50%) enhancement in vegetative growth again occurred in N addition shrubs. Results of this study strongly suggest that soil N availability produces strong positive bottom-up effects on the reproductive output of the alpine shrub C. oppositifolia. Despite taking considerably longer to be manifest in comparison to the previously reported top-down indirect negative effects of lizard predators in the same study system, our results suggest that both bottom-up and top-down forces are important in controlling the reproductive output of an alpine shrub.

Addressing the challenges posed by high complexity, ambiguity in model structure determination, and overfitting during parameter learning in the soft sensor modeling of intricate industrial processes, this study introduces a recursive interval type-2 fuzzy neural network utilizing a logarithmic (log) t-norm (RIT2FNN-log). Unlike the conventional interval type-2 fuzzy neural network that employs the product t-norm, which often results in insufficient excitation intensity and inaccurate numerical calculations, the proposed logarithmic t-norm enhances excitation intensity and magnifies subtle differences, thereby enhancing network accuracy. The optimization of RIT2FNN-log involves two main stages: structural learning and parameter learning. Structural learning employs an arithmetic optimization algorithm with mean square error as the fitness function to determine the optimal number of rules and initial parameter values for RIT2FNN-log. Parameter learning utilizes a hybrid learning algorithm, with the antecedent parameters trained using a gradient descent algorithm based on AdaBound to prevent overfitting, and the consequent parameters trained using the least squares method based on recursive singular value decomposition for rapid model convergence. The efficacy of RIT2FNN-log was demonstrated through applications in modeling tasks combined cycle power plant full-load output power prediction and Box-Jenkins time series analysis. Experimental comparisons with existing models revealed superior performance of RIT2FNN-log in terms of simplicity in network structure and lower mean square error.

Studies on plant-pollinator interactions have largely neglected the potential negative effects of the predators of pollinators on seed output, even though anti-predatory behaviour of pollinators may affect visitation patterns, pollen transfer, and therefore potentially, plant reproductive output. We tested the hypothesis that the presence of lizards and insectivorous birds, by reducing pollinator visitation, can have significant negative effects on seed output in the insect-pollinated, genetically self-incompatible lower alpine Andean shrub, Chuquiraga oppositifolia (Asteraceae). The lower alpine belt supports a high density of territorial Liolaemus (Tropiduridae) lizards and low shrubs interspersed among rocks of varying sizes, the latter inhabited by lizards and commonly used by flycatchers Muscisaxicola (Tyrannidae) as perching sites. In a 2×2 factorial predator-exclusion experiment, visitation rates of the most frequent pollinators of C. oppositifolia (the satyrid butterfly Cosmosatyrus chilensis and the syrphid fly Scaeva melanostoma), the duration of pollinator visits, and seed output, were 2–4 times greater when lizards were excluded, while birds had no effect. In a natural experiment, visits by S. melanostoma were 9 times shorter, and pollinator visitation rates of C. chilensis and S. melanostoma, and C. oppositifolia seed output were 2–3 times lower on shrubs growing adjacent to lizard-occupied rocks compared to those growing distant from rocks. Our results, verified for additional Andean sites, suggest that lizard predators can alter the behaviour of pollinators and elicit strong top-down indirect negative effects on seed output. Such effects may be especially important in high alpine plant communities, where pollinator activity can be low and erratic, and pollen limitation has been reported.

This paper presents a new and improved method of hybrid input-output (I-O) to evaluate the economic impact of a biomass power plant’s resource production and consumption. The effect of resource consumption induces a change in an economy’s production structure and alters the technical coefficient of the hybrid I-O table, which should not be changed. Our study examines this problem based on two cases: a small cut-off ratio from the existing industry, in which the change in the technical coefficient can be ignored, and a large cut-off ratio from the existing industry, in which the technical coefficient is amended using the pre-adjustment method. Consequently, the biomass power plant using the large cut-off ratio from the existing industry case contributes about 1114 million yen to the local economy. A comparison of these two cases shows that the error caused by ignorance of the technical coefficient adjustment could result in 291.78 million yen less in total economic production, which is about 36% of the total additional production using the small cut-off ratio from the existing industry case. The losses in total economic production clearly increase in economic sectors that are related to the resource consumption of the biomass power plant.