Learner Model Research Articles

Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption

This study utilized different methods, namely classical multiple linear regression (MLR), statistical approach exponential smoothing (EXPS), and deep learning algorithm long short-term memory (LSTM) to forecast long-term electricity consumption in the Kingdom of Saudi Arabia. The originality of this research lies in (1) specifying exogenous variables that significantly affect electrical consumption; (2) utilizing the Bayesian optimization algorithm (BOA) to develop individual super learner BOA-LSTM models for forecasting the residential and total long-term electric energy consumption; (3) measuring forecasting performances of the proposed super learner models with classical and statistical models, viz. MLR and EXPS, by employing the broadly used evaluation measures regarding the computational efficiency, model accuracy, and generalizability; and finally (4) estimating forthcoming yearly electric energy consumption and validation. Population, gross domestic products, imports, and refined oil products significantly impact residential and total annual electricity consumption. The coefficient of determination (R2) for all the proposed models is greater than 0.93, representing an outstanding fitting of the models with historical data. Moreover, the developed BOA-LSTM models have the best performance with R2>0.99, enhancing the predicting accuracy (Mean Absolute Percentage Error (MAPE)) by 59.6% and 54.8% compared to the MLR and EXPS models, respectively, of total annual electricity consumption. This forecasting accuracy in residential electricity consumption for the BOA-LSTM model is improved by 62.7% and 68.9% compared to the MLR and EXPS models. This study achieved a higher accuracy and consistency of the proposed super learner model in long-term electricity forecasting, which can be utilized in energy strategy management to secure the sustainability of electric energy.

Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery.

Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications.

Research on personalized recommendation method of preschool e-learning resources

Abstract E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8. In contrast, the detection rate of traditional methods is between 0.6 and 0.7. The collaborative filtering recommendation method outperforms the other two methods regarding completeness, response time, accuracy, and F1 value. In terms of satisfaction, the collaborative filtering recommendation algorithm had the highest satisfaction rate, followed by the learner model; the collaborative filtering method had the best results in improving learning performance. Therefore, personalized recommendation methods can improve learning efficiency and reduce retrieval time, and the collaborative filtering recommendation method is the most effective among the two methods proposed in this paper.

Evaluation of Online Learners’ Learning Performance Based on Fuzzy Borda Method

Since the traditional evaluation system of learning focuses only on the summative evaluation of learners’ test scores, ignoring the process evaluation of learners in learning process, the evaluation system should not only consider the content of the online learning process but also that of face-to-face classroom teaching process to evaluate learners’ learning performance in multiple dimensions and levels. This approach evaluates online learning more reasonably, operably and feasibly. To overcome limitation of using more than one method in the evaluation of learning performance, the learning performance of online learners was first evaluated using the principal component analysis method, entropy method, comprehensive indicator method, and TOPSIS method. Based on these four evaluation results, a combination evaluation model of online learners’ learning performance was constructed. The Fuzzy Borda method was used to evaluate nine administrative classes of accounting majors in three colleges and universities in Hainan. The results show that simply using a single evaluation method to evaluate the learning performance of online learners has defects, and the evaluation results are too biased. By using the Fuzzy Borda method, multiple evaluation methods can be combined, allowing a single evaluation method to realize complementary advantages and obtain more comprehensive and credible evaluation results. Using the Fuzzy Borda method to systematically evaluate the learning performance of online learners improves the scientificity of evaluation results and provides a new idea and method for the evaluation of online learning performance. The results of this study have significant reference value for using scientific evaluation methods and objective data to evaluate online learning performance, rank online learners scientifically, and summarize teaching experience to improve online learning performance.

Research on Personalized Video Matching Algorithm Based on Implicit Feature Transfer and PTransE

With the rapid development of internet plus education, it is increasingly important to quickly and accurately match personalized videos for learners from massive learning videos. However, the existing resource recommendation methods have two problems. On the one hand, they do not make full use of the implicit interaction data of learners in the learning process, i.e., they rarely reflect the comprehensive consideration of learners' interest preferences and cognitive level when describing learners. On the other hand, they are mostly based on collaborative filtering algorithms, using the similarity between learners or videos to recommend, ignoring the impact of semantic relations between videos on the recommendation results. In view of this, we proposed a personalized learning video matching method (IFT‐PTransE) based on heterogeneous feature data transfer and knowledge reasoning from two aspects of learning behavior data mining and knowledge graph representation learning. In this method, firstly, a learner model is constructed. By analyzing and quantifying various implicit interaction behavior data of learners, the sparse video scoring matrix is filled as auxiliary data to complete the transfer of target scoring data. Secondly, the semantic close relationship between videos is introduced. PTransE algorithm is used to mine the multi‐path relationship between entities. All entity relationships are embedded into the low dimensional vector space, so that the semantic similarity between videos can be calculated using the distance between vectors. Finally, video score similarity and semantic similarity between frequencies are fused. video sorting is performed based on the improved collaborative filtering algorithm and then recommend the top N videos to the students. Through simulation and analysis, the effectiveness of this method in personalized video matching is proved. This method makes up for the shortcomings of collaborative filtering algorithm in using implicit information, enhances the recommendation effect at the semantic level, and solves the data sparse and cold start problems to a certain extent. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Exploring the effects of gender in skills acquisition in collaborative learning based on the ontological clustering model

AbstractBackgroundProject‐based collaborative learning (PBCL) is a technique that supports knowledge and skill development through complex, real‐world projects. Understanding factors that influence group performance in PBCL, such as gender composition, is crucial.ObjectivesThis study investigates the impact of gender composition on group performance in PBCL, specifically focusing on the critical thinking, creativity, communication, and collaboration (4Cs) 21st‐century learning skills.MethodsA total of 312 students were divided into distinct Python programming groups for the experiment. Group performance was assessed using an agglomerative clustering algorithm guided by the ontological group learner model, which examined gender composition, communication patterns, collaboration strategies and project assessment outcomes.Results and ConclusionGender‐balanced groups demonstrated positive outcomes in the 4Cs skills, with groups containing a greater proportion of women exhibiting superior performance in collaboration and communication, whereas majority‐male groups performed relatively poorer across all skills. Notably, in the context of PBCL, the presence of more women in small groups enhanced the 4Cs skills project assessment outcomes.TakeawaysFuture research should focus on providing effective support for male online learners in developing the 4Cs skills. The findings offer insights and recommendations for group formation in collaborative learning, facilitating the design of inclusive and impactful learning environments.

Sensor-based indoor air temperature prediction using deep ensemble machine learning: An Australian urban environment case study

Accurate prediction of indoor temperature is critical for climate change adaptation and occupant health. The aim of this study is to investigate an improved deep ensemble machine learning framework (DEML), by adjusting the model architecture with several machine learning (ML) and deep learning (DL) approaches to forecast the sensor-based indoor temperature in the Australian urban environment. We collected ambient station-based temperatures, satellite-based outdoor climate characteristics, and low-cost sensor-based indoor environmental metrics from 96 devices from August 2019 to November 2022, and established DEML with a rolling windows approach to assess the prediction stability over time. The DEML model was compared with several benchmark models, including Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGboost), Long-short term memory (LSTM), and Super Learner model (SL). A total of 13,715 days [median: 341 days; IQR (the interquartile range): 221–977 days] of low-cost sensor-based indoor temperature were included in 25 commercial and residential buildings across eight cities. The prediction performance of DEML was superior to the other five benchmark models in most of the sensors [coefficients of determination (R2) of 0.861–0.990 and root mean square error (RMSE) of 0.125–0.886 °C], followed by RF and SL algorithms. DEML consistently achieved high accuracy across different climate zones, seasons, and building types, which could be used as a crucial tool for optimizing energy use, maintaining occupant comfort and health, and adapting to the impacts of climate change.

Short-Term PM2.5 Concentration Changes Prediction: A Comparison of Meteorological and Historical Data

Machine learning is being extensively employed in the prediction of PM2.5 concentrations. This study aims to compare the prediction accuracy of machine learning models for short-term PM2.5 concentration changes and to find a universal and robust model for both hourly and daily time scales. Five commonly used machine learning models were constructed, along with a stacking model consisting of Multivariable Linear Regression (MLR) as the meta-learner and the ensemble of Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as the base learner models. The meteorological datasets and historical PM2.5 concentration data with meteorological datasets were preprocessed and used to evaluate the model’s accuracy and stability across different time scales, including hourly and daily, using the coefficient of determination (R2), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE). The results show that historical PM2.5 concentration data are crucial for the prediction precision of the machine learning models. Specifically, on the meteorological datasets, the stacking model, XGboost, and RF had better performance for hourly prediction, and the stacking model, XGboost and LightGBM had better performance for daily prediction. On the historical PM2.5 concentration data with meteorological datasets, the stacking model, LightGBM, and XGboost had better performance for hourly and daily datasets. Consequently, the stacking model outperformed individual models, with the XGBoost model being the best individual model to predict the PM2.5 concentration based on meteorological data, and the LightGBM model being the best individual model to predict the PM2.5 concentration using historical PM2.5 data with meteorological datasets.

Comparative study on landslide susceptibility mapping based on different ratios of training samples and testing samples by using RF and FR-RF models

Evaluation of landslide susceptibility is essential to planning of land and space utilization. For this purpose, the paper presents a case study from Fugu County, Shaanxi Province, China. Firstly, the geological environment and current state of landslides in Fugu County were investigated. Then, slope, aspect, terrain relief, curvature, lithology, land type, and normalized difference vegetation index (NDVI) were considered as the landslide susceptibility condition factors, and the correlation between these carried out by using Multicollinearity Analysis method. Next, landslide and non-landslide samples were divided into training samples and testing samples according to the sample ratios of 8/2, 7/3, 6/4, and 5/5, respectively. The landslide susceptibility mapping was carried out by using Random Forest (RF) model and Frequency Ratio coupled with Random Forest (FR-RF) model, respectively. Lastly, the landslide density (LD), landslide frequency ratio (LFR), the area under the curve (AUC) of the receiver operator, and other indicators were used to validate the rationality, accuracy, and performance of the landslide susceptibility maps produced from different models and ratios. The results indicated that all maps are reasonable, except the map when ratio is 5/5. For each map, regardless of ratios, the LD and LFR are the greatest in the zones classed as having a very high susceptibility, followed by those with a high, moderate, low, and very low classes.In the Random Forest (RF) model, when the training test set is not at the same time its in the area of extremely high sensitivity of LD and the size of the FR value respectively 7/3 (201.026) ​> ​8/2 (154.440) ​> ​6/4 (93.696) >5/5 (136.364) and 7/3 (4.806) ​> ​8/2 (3.692) ​> ​6/4 (3.260) ​> ​5/5 (2.240); in the Frequency Ratio coupled with Random Forest (FR-RF) model, Inall the training test sets the size of the proportion of LD and FR value respectively 7/3 (145.693) ​> ​6/4 (127.151) ​> ​5/5 (122.857) ​> ​8/2 (113.263) and 7/3 (3.334) ​> ​6/4 (3.073) ​> ​5/5 (2.811) ​> ​8/2 (2.592). What else, from the comparison of ROC curves, when ratio is 7/3, the accuracy of the two models is higher than that of other ratios. Similarly, the results of the ensemble model (A combination of two models with different learning abilities.) are not more reasonable than the results of the single model, which reflects that the combination of a weaker learner model (Frequency Ratio model here) with a stronger learner model (Random Forest model here) can diminish the performance of the stronger model.

Case study of designing and evaluating an independent open learner model tool

Case study of designing and evaluating an independent open learner model tool

Super learner ensemble model: A novel approach for predicting monthly copper price in future

Companies and governments dependent on copper mining need to be able to predict copper prices in order to make important decisions. Despite the nonlinear and nonstationary nature of copper prices, their periods may vary as they fluctuate due to potential growth, cyclical fluctuations and errors. A trend-cycle refers to the combination of trend and cyclical components. Trend-cycles are characterized by different characteristics, which are crucial to making predictions. Therefore, this study focuses on developing and proposing a novel model based on an ensemble machine learning technique to predict monthly copper prices in the future by using different soft computing methods, including multi-layer perception (MLP) neural network, support vector regression (SVR), and extreme gradient boosting (XGBoost). The monthly copper price dataset from August 2001 to August 2021 was gathered for this aim based on the 14 effective parameters. These parameters were selected based on the suggestions of previous research. The main novelty of this study is the development most accurate model to predict monthly copper prices using Cubist algorithm-based super learner model as the new predictive system. The results indicated that the proposed super learner models outperformed of MLP, SVR, and XGBoost models based on the determination coefficient (R-squared), value account for (VAF), root mean square of errors (RMSE), Accuracy (Acc) and Mean Absolute Relative Error (MARE). A comprehensive comparison of different artificial intelligence models demonstrates that MLP neural network is the best model for predicting monthly copper prices. However, the standalone models involving MLP, SVR, and XGBoost, presented higher error with an RMSE in the interval of [278.3826 - 502.6946], and MARE in the interval of [0.056 - 0.1277]. Hence, Cubist-based super learner can be employed as a reliable system to predict monthly copper prices in the future. Besides, this study presents a rational mathematical model based on gene expression programming (GEP) for copper price prediction in future and for use by other researchers. Noteworthy, the final step of the study was sensitivity analysis conducting, which the results revealed that “lead price” and “euro to USD” parameters have respectively the lowest and highest impact on the monthly copper price.

Multi-view computable online learner modeling by heterogeneous network: An AI-enabled perspective

To support accurate personalized online intelligent education applications in the big data environment, computable online learner modeling is very important. It can accurately capture the unique needs and characteristics of learners in a computable way and provides customized learning experiences. We propose a structured semantic embedding-based approach for online learner modeling, which considers both the multidimensional learners' characteristics and heterogeneous information on the learning context to develop a comprehensive learner model. Specifically, an AI-enabled heterogeneous information network embedding technique is employed to encode complications of different learners' characteristics and rich relationships between them, which integrate different explicit characteristic information and implicit association information about inherent multidimensional interactions for computable learners modeling. Extensive experimental results and evaluations demonstrate that the proposed model achieves the optimal performance on the Precision@N and Recall@N metrics in course recommendation tasks, and the Accuracy and F−score metrics in learner clustering tasks are also better than other baseline models, which prove the ability to boost the performance of downstream tasks by incorporating and capturing the underlying semantic and structural information among different types of nodes to fuse the embedding for learner modeling.

Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analytics

AbstractBackgroundWith increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction.ObjectiveThe paper reviews the current research on intelligent learning technology designed to make models of student learning and instruction transparent to teachers, an area we call model‐based learning analytics. We intended to gain an insight into the coupling between the knowledge models that underpin the intelligent system and the knowledge used by teachers in their classroom decision making.MethodsUsing a systematic literature review methodology, we first identified 42 papers, mainly from the domain of intelligent tutoring systems and learning analytics dashboards that conformed to our selection criteria. We then qualitatively analysed the context in which the systems were applied, models they used and benefits reported for teachers and learners.Results and ConclusionsA majority of papers used either domain or learner models, suggesting that instructional decisions are mostly left to teachers. Compared to previous reviews, our set of papers appeared to have a stronger focus on providing teachers with theory‐driven insights and instructional decisions. This suggests that model‐based learning analytics can address some of the shortcomings of the field, like meaningfulness and actionability of learning analytics tools. However, impact in the classroom still needs further research, as in half of the cases the reported benefits were not backed with evidence. Future research should focus on the dynamic interaction between teachers and technology and how learning analytics has an impact on learning and decision making by teachers and students. We offer a taxonomy of knowledge models that can serve as a starting point for designing such interaction.

Gamification and open learner model: An experimental study on the effects on self-regulatory learning characteristics

Gamification and open learner model: An experimental study on the effects on self-regulatory learning characteristics

Foundations for Human-AI teaming for self-regulated learning with explainable AI (XAI)

This discussion takes a human-centred perspective of the contributions of the collection. Its papers explore diverse, new uses of AI with rich, multimedia sensor data towards new ways to measure and understand self-regulated learning. This work can contribute to the learning sciences. It can also provide a foundation for future personalised teaching and learning systems with explainable AI (XAI) and learner control. I will discuss the papers from that perspective with a focus on an important form of XAI in education – the Open Learner Models (OLM). When suitably designed, OLMs can empower a learner to: (1) contribute data about themself and their self-regulated learning processes, complementing conventional and multimedia data; (2) scrutinise and control learner data collection and use in AI-based systems and (3) be the controlling partner in AI-teaming that scaffolds their self-regulated learning processes.

A novel approach to improving colonoscopy learning efficiency through a colonoscope roaming system: randomized controlled trial.

Colonoscopy is indispensable in the diagnosis and treatment of lower digestive tract (LDT) diseases. Skilled colonoscopists are in great demand, but it takes considerable time for beginners to become experts. In addition, patients may refuse to permit primary learners to practise colonoscopy on them. Thus, improving the instructional programmes and models for primary learners is a key issue in endoscopy training. Convenience and a self-paced, learner-centred approach make e-learning an excellent instructional prospect. Therefore, we created the Colonoscope Roaming System (CRS) to assist in colonoscopy teaching procedures. We aimed to develop the e-learning software, test it with beginner colonoscopists and evaluate its effectiveness via subjective and objective methods. Through a randomized controlled trial, participants were randomly allocated to an e-learning group (EG) or a control group (CG) after a pretest evaluation. The CG learned through the traditional colonoscopy teaching mode, while the EG used CRS in addition to the traditional teaching mode. Subsequent to the training, the participants completed a posttest and colonoscopy examination. The EG also completed a satisfaction questionnaire. Of the 84 participants, 81 (96%) finished the colonoscopy learning and evaluation modules of the CRS. No conspicuous differences in the pretest scores were found between the EG and CG (p>0.05). Two months later, the posttest scores for the EG were higher than those of the CG (p<0.001), and the EG had better performance on the colonoscopy examination (p<0.01). Overall, 86.25% of questions raised in Q1-Q20 were satisfied with the CRS and considered it successful. The use of CRS may be an effective approach to educate beginner colonoscopists to attain skills.

DEVELOPMENT OF A DIGITAL EDUCATIONAL ENVIRONMENT IN CHINA

Current model design research in the field of personalized learning mainly includes three categories: personalized learner model, personalized learning resource recommendation model, and personalized learning path model. The development of a personalized student model mainly depends on the basic information of the student, his interests and preferences, learning style, behavioral characteristics, etc.; and all learner information and data are also used to develop and implement a personalized learning resource recommendation model and an individualized learning path model Essential framework. The learner model is developed through an in-depth analysis of the learner's main attributes, learning process and learning outcomes, and on this basis, a model of the learning path that fits the learner is planned and personalized learning resources are presented. Among the various developed models, the student model occupies the largest share. In the study of personalized learning, different models can be developed that correspond to the individual characteristics and actual needs of students, so as to be more in line with the concept of respecting the individual characteristics of students, thereby contributing to the high-quality development of individualized learning and the overall development of students. We also described the model with tables that are given in the article.

Utilizing machine learning models to estimate energy savings from an industrial energy system

Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.

Lifetime prediction of electronic devices based on the P-stacking machine learning model

Nowadays the data-driven artificial intelligence (AI) and machine learning (ML) provide novel approaches for the effective lifetime prediction of the electronic devices with complicated mechanisms and multiple controlling factors. However, the existing ML prediction models cannot process both high accuracy and high efficiency at the meantime. To address this problem, this paper proposes the new P-Stacking ML algorithm model, which combines the Pearson correlation analysis and the Stacking multi-model fusion. The Pearson correlation analysis is first applied to select the multiple base learner models with weak correlations for the following Stacking multi-model fusion. Two neutral network layers are built to perform the Stacking algorithm. In this work, two distinct types of electronic devices are utilized to verify the effectiveness of the P-Stacking ML model, which are the IGBT devices and the lithium-ion batteries respectively. For the IGBT lifetime prediction, the results have shown that compared with the long-term and short-term memory neural network (LSTM) model in the previous literature, the mean square error (MSE) of the P-Stacking ML model is improved by 6 %, and the average model training time is reduced by 90 %. Moreover, for the lithium batteries, the lifetime prediction accuracy of the P-Stacking model is increased by 65 %, and the training time is decreased by 90 %. The results demonstrated that the P-Stacking ML model can significantly improve both the prediction accuracy and efficiency simultaneously.

Güneş ve Rüzgar Enerjisinden Elektrik Üretiminin Gün Öncesi Piyasa Takas Fiyatlarına ve Fiyat Volatilitesine Etkisi: Türkiye Örneği

Solar and wind generation are the primary tools to prevent climate change and high carbon emissions. Due to their intermittent generation characteristics, solar and wind power plants have a different impact on the market-clearing price formation compared to conventional generation sources. The paper investigates the solar and wind generation effect on the day ahead market-clearing price in Turkey between the 2016 and 2022. We used a 2nd order polynomial learner model to analyze the impact of solar and wind generation level on the market-clearing price level and volatility. We find that Dutch TTF increases market-clearing price with a coefficient of 0.24. An increase in wind and solar generation reduce the market-clearing price. Solar generation is ineffective on the market-clearing price below a certain demand level. Wind generation reduces market-clearing price with a 37.78 coefficient at low demand levels and a 6.55 coefficient at high demand levels. Solar generation has a price-reducing effect with 5.55 at high demand levels. Finally, Dutch TTF and wind generation increased volatility with coefficients of 0.04 and 0.69; solar generation reduced volatility with a coefficient of 0.83.