Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine

This article presents a novel hybrid approach using statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting. The proposed methodology was constructed using hourly data from Ukraine's electricity consumption ranging from 2013 to 2020. To this end, we analysed the underlying structure of the hourly, daily and yearly time series of electricity consumption. The long-term yearly trend is evaluated using macroeconomic regression analysis. The mid-term model integrates temperature and calendar regressors to describe the underlying structure, and combines ARIMA and LSTM ``black-box'' pattern-based approaches to describe the error term. The short-term model captures the hourly seasonality through calendar regressors and multiple ARMA models for the residual. Results show that the best forecasting model is composed by combining multiple regression models and a LSTM hybrid model for residual prediction. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution. In two years of out-of-sample forecasts with 17520 timesteps, it is shown to be within 96.83 \% accuracy.Comment: 31 pages, 13 figures, submitted to Applied Energ

A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings

This paper presents a new algorithm to extract device profiles fully unsupervised from three phases reactive and active aggregate power measurements. The extracted device profiles are applied for the disaggregation of the aggregate power measurements using particle swarm optimization. Finally, this paper provides a new approach for short term power predictions using the disaggregation data. For this purpose, a state changes forecast for every device is carried out by an artificial neural network and converted into a power prediction afterwards by reconstructing the power regarding the state changes and the device profiles. The forecast horizon is 15 minutes. To demonstrate the developed approaches, three phase reactive and active aggregate power measurements of a multi-tenant commercial building are used. The granularity of data is 1 s. In this work, 52 device profiles are extracted from the aggregate power data. The disaggregation shows a very accurate reconstruction of the measured power with a percentage energy error of approximately 1 %. The developed indirect power prediction method applied to the measured power data outperforms two persistence forecasts and an artificial neural network, which is designed for 24h-day-ahead power predictions working in the power domain.Comment: 15 pages, 14 figures, 4 table

Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management

Trends in invasive bacterial diseases during the first 2 years of the COVID-19 pandemic: analyses of prospective surveillance data from 30 countries and territories in the IRIS Consortium.

BACKGROUND The Invasive Respiratory Infection Surveillance (IRIS) Consortium was established to assess the impact of the COVID-19 pandemic on invasive diseases caused by Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis, and Streptococcus agalactiae. We aimed to analyse the incidence and distribution of these diseases during the first 2 years of the COVID-19 pandemic compared to the 2 years preceding the pandemic. METHODS For this prospective analysis, laboratories in 30 countries and territories representing five continents submitted surveillance data from Jan 1, 2018, to Jan 2, 2022, to private projects within databases in PubMLST. The impact of COVID-19 containment measures on the overall number of cases was analysed, and changes in disease distributions by patient age and serotype or group were examined. Interrupted time-series analyses were done to quantify the impact of pandemic response measures and their relaxation on disease rates, and autoregressive integrated moving average models were used to estimate effect sizes and forecast counterfactual trends by hemisphere. FINDINGS Overall, 116 841 cases were analysed: 76 481 in 2018-19, before the pandemic, and 40 360 in 2020-21, during the pandemic. During the pandemic there was a significant reduction in the risk of disease caused by S pneumoniae (risk ratio 0·47; 95% CI 0·40-0·55), H influenzae (0·51; 0·40-0·66) and N meningitidis (0·26; 0·21-0·31), while no significant changes were observed for S agalactiae (1·02; 0·75-1·40), which is not transmitted via the respiratory route. No major changes in the distribution of cases were observed when stratified by patient age or serotype or group. An estimated 36 289 (95% prediction interval 17 145-55 434) cases of invasive bacterial disease were averted during the first 2 years of the pandemic among IRIS-participating countries and territories. INTERPRETATION COVID-19 containment measures were associated with a sustained decrease in the incidence of invasive disease caused by S pneumoniae, H influenzae, and N meningitidis during the first 2 years of the pandemic, but cases began to increase in some countries towards the end of 2021 as pandemic restrictions were lifted. These IRIS data provide a better understanding of microbial transmission, will inform vaccine development and implementation, and can contribute to health-care service planning and provision of policies. FUNDING Wellcome Trust, NIHR Oxford Biomedical Research Centre, Spanish Ministry of Science and Innovation, Korea Disease Control and Prevention Agency, Torsten Söderberg Foundation, Stockholm County Council, Swedish Research Council, German Federal Ministry of Health, Robert Koch Institute, Pfizer, Merck, and the Greek National Public Health Organization

Effect of cytomegalovirus infection on breastfeeding transmission of HIV and on the health of infants born to HIV-infected mothers

Cytomegalovirus (CMV) infection can be acquired in utero or postnatally through horizontal transmission and breastfeeding. The effect of postnatal CMV infection on postnatal HIV transmission is unknown

Evaluating Nurses' Implementation of an Infant-Feeding Counseling Protocol for HIV-Infected Mothers: The Ban Study in Lilongwe, Malawi

A process evaluation of nurses’ implementation of an infant-feeding counseling protocol was conducted for the Breastfeeding, Antiretroviral and Nutrition (BAN) Study, a prevention of mother-to-child transmission of HIV clinical trial in Lilongwe, Malawi. Six trained nurses counseled HIV-infected mothers to exclusively breastfeed for 24 weeks postpartum and to stop breastfeeding within an additional four weeks. Implementation data were collected via direct observations of 123 infant feeding counseling sessions (30 antenatal and 93 postnatal) and interviews with each nurse. Analysis included calculating a percent adherence to checklists and conducting a content analysis for the observation and interview data. Nurses were implementing the protocol at an average adherence level of 90% or above. Although not detailed in the protocol, nurses appropriately counseled mothers on their actual or intended formula milk usage after weaning. Results indicate that nurses implemented the protocol as designed. Results will help to interpret the BAN Study’s outcomes

Adherence to extended postpartum antiretrovirals is associated with decreased breast milk HIV-1 transmission

Estimate association between postpartum antiretroviral adherence and breastmilk HIV-1 transmissio

Plasma Micronutrient Concentrations Are Altered by Antiretroviral Therapy and Lipid-Based Nutrient Supplements in Lactating HIV-Infected Malawian Women

Background: Little is known about the influence of antiretroviral therapy with or without micronutrient supplementation on the micronutrient concentrations of HIV-infected lactating women in resource-constrained settings

Integration and Dimensioning of Battery Storage Systems in Commercial Building Applications with Renewable Powerplants and Battery Electric Vehicles

In this study, the integration and dimensioning of battery storage systems (BSS) in commercial buildings is investigated under consideration of renewable energy generation and electromobility. For this purpose, an energy system model has been developed and several energy system constellations (scenarios) were simulated within a case study for the years 2020 and 2030. Two modeling approaches for the charging of battery electric vehicles were used: simple, uncontrolled and smart, controlled charging. For each scenario a BSS could be implemented and dimensioned by the optimizer with the aim to reduce overall system costs. This could be achieved by either a reduction of peak loads (“peak shaving”) of the building and the battery electric vehicles (BEV) or by an increase of the self-consumption rate of the photovoltaic powerplant. The results of the case study show, that in each scenario a BSS has a positive impact on the optimization result. Overall energy system costs could be reduced by the integration of a BSS in all cases. This concludes that already today BSS have the potential for an economic integration into commercial buildings – and even more in the future with decreasing storage costs and incentives. Furthermore, they can support a grid-friendly integration of BEV and local renewable energy generation