Modified artificial neural network based on developed snake optimization algorithm for short-term price prediction

Short-term prices prediction is a crucial task for participants in the electricity market, as it enables them to optimize their bidding strategies and mitigate risks. However, the price signal is subject to various factors, including supply, demand, weather conditions, and renewable energy sources, resulting in high volatility and nonlinearity. In this study, a novel approach is introduced that combines Artificial Neural Networks (ANN) with a newly developed Snake Optimization Algorithm (SOA) to forecast short-term price signals in the Nord Pool market. The snake optimization algorithm is utilized to optimize both the structure and weights of the neural network, as well as to select relevant input data based on the similarity of price curves and wind production. To evaluate the effectiveness of the proposed technique, experiments have been conducted using data from two regions of the Nord Pool market, namely DK-1 and SE-1, across different seasons and time horizons. The results demonstrate that the proposed technique surpasses two alternative methods based on Particle Swarm Optimization (PSO) and Genetic Algorithms-based Neural Network (PSOGANN) and Gravitational Search Optimization Algorithm-based Neural Network (GSONN), exhibiting superior accuracy and minimal error rates in short-term price prediction. The results show that the average MAPE index of the proposed technique for the DK-1 region is 3.1292%, which is 32.5% lower than the PSOGA method and 47.1% lower than the GSONN method. For the SE-1 region, the average MAPE index of the proposed technique is 2.7621%, which is 40.4% lower than the PSOGA method and 64.7% lower than the GSONN method. Consequently, the proposed technique holds significant potential as a valuable tool for market participants to enhance their decision-making and planning activities.

A time series based machine learning strategy for wastewater-based forecasting and nowcasting of COVID-19 dynamics

Monitoring COVID-19 infection cases has been a singular focus of many policy makers and communities. However, direct monitoring through testing has become more onerous for a number of reasons, such as costs, delays, and personal choices. Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring disease prevalence and dynamics to supplement direct monitoring. The objective of this study is to intelligently incorporate WBE information to nowcast and forecast new weekly COVID-19 cases and to assess the efficacy of such WBE information for these tasks in an interpretable manner. The methodology consists of a time-series based machine learning (TSML) strategy that can extract deeper knowledge and insights from temporal structured WBE data in the presence of other relevant temporal variables, such as minimum ambient temperature and water temperature, to boost the capability for predicting new weekly COVID-19 case numbers. The results confirm that feature engineering and machine learning can be utilized to enhance the performance and interpretability of WBE for COVID-19 monitoring, along with identifying the different recommended features to be applied for short-term and long-term nowcasting and short-term and long-term forecasting. The conclusion of this research is that the proposed time-series ML methodology performs as well, and sometimes better, than simple predictions that assume available and accurate COVID-19 case numbers from extensive monitoring and testing. Overall, this paper provides an insight into the prospects of machine learning based WBE to the researchers and decision-makers as well as public health practitioners for predicting and preparing the next wave of COVID-19 or the next pandemic.

Research on renewable energy prediction technology: empirical analysis for Argentina and China

Our world needs to develop clean energy to reach the target of carbon peak and carbon neutralization. As one of clean energy, wind energy should contribute to energy conservation and emission reduction. Wind power generation is an important field of wind energy application. However, the fluctuation and intermittency of wind can affect the safety of power system. Therefore, prediction of wind power accurately for wind power safety, dispatching, and power grid development is significant. This paper proposes a prediction model of wind power, and predicts the wind power of two wind farms. For the complex wind speed series, the variational modal decomposition (VMD) method is used to reduce its volatility before prediction. And this paper presents an improved method to improve the prediction efficiency when least square support vector machine (LSSVM) predicts stationary series. The prediction result shows that the proposed model improves the prediction of wind power effectively, provides an effective method for wind farm to predict the wind power, and makes contributions to reducing carbon emissions and environmental protection.

HbA1c variability in type 2 diabetes is associated with the occurrence of new-onset albuminuria within three years

AIMS

To evaluate the association between HbA1c coefficient of variation (HbA1c-CV) and 3-year new-onset albuminuria risk.

METHODS

A retrospective cohort study involving 716 normoalbuminuric type 2 diabetes patients was conducted between 2010 and 2014. HbA1c-CV was used to categorize patients into low, moderate or high variability groups. Multivariate logistic models were constructed and validated. Integrated discrimination (IDI) and net reclassification (NRI) improvement indices were used to quantify the added predictive value of HbA1c-CV.

RESULTS

The mean age of our cohort was 56.1±12.9years with a baseline HbA1c of 8.3±1.3%. Over 3-years of follow-up, 35.2% (n=252) developed albuminuria. An incremental risk of albuminuria was observed with moderate (6.68-13.43%) and high (above 13.44%) HbA1c-CV categories demonstrating adjusted odds ratios of 1.63 (1.12-2.38) and 3.80 (2.10-6.97) for 3-year new-onset albuminuria, respectively. Including HbA1c-CV for 3-year new-onset albuminuria prediction improved model discrimination (IDI: 0.023, NRI: 0.293, p<0.05). The final model had a C-statistic of 0.760±0.018 on validation.

CONCLUSION

HbA1c-CV improves 3-year prediction of new-onset albuminuria. Together with mean HbA1c, baseline urine albumin-to-creatinine ratio and presence of hypertension, accurate 3-year new-onset albuminuria prediction may be possible.