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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

With climate change driving the global push toward sustainable energy, the reliability of power systems increasingly depends on accurate forecasting methods. This study examined the role of machine learning (ML) in forecasting solar PV power output (SPVPO) and wind turbine power output (WTPO) and identified the challenges posed by the intermittent nature of these renewable energy sources. This study examined the current techniques, challenges, and future directions in ML-based forecasting of SPVPO and WTPO and proposed a standardized framework. Using the Mann–Whitney and Kruskal–Wallis tests, the results highlight the significant impact of key meteorological and operational variables on enhancing forecasting accuracy, as measured by MAPE and R-squared. Key features for SPVPO forecasting include solar irradiance, ambient temperature, and prior SPVPO, while wind speed, turbine speed, and prior wind power output are crucial for WTPO forecasting. Moreover, ensemble models, support vector machines, Gaussian processes, hybrid artificial neural networks, and decomposition-based hybrid models exhibit promising forecasting accuracy and reliability. Challenges such as data availability, complexity-interpretability trade-offs, and integration difficulties with energy management systems present opportunities for innovative solutions. These include exploring advanced data processing and calibration techniques, leveraging Big Data and IoT advancements, formulating advanced machine learning (ML) techniques, and employing probabilistic approaches with desirable accuracy and robustness in forecasting solar photovoltaic power output (SPVPO) and wind turbine power output (WTPO). Additionally, expanding research to ensure model generalizability across diverse climate conditions and forecasting horizons is crucial for enhancing the reliability and efficiency of renewable energy forecasting using machine learning techniques.

A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal Experiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each grid point using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of week-3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, sub-Saharan Africa and Southeast Asia, and over subtropical South America, Africa, and Australia. Near-surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America, and East Africa in all seasons. An example real-time week-3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden–Julian oscillation event as well as weak–moderate ongoing La Niña event.

With the prolonged COVID-19 pandemic worldwide, lifestyles have totally changed and the characteristics of waste generation have also changed accordingly. Among the various wastes related to COVID-19, waste personal protective equipment (PPE), which was used to prevent infection of COVID-19, can be an indirect route for the infection of COVID-19. Hence, it requires proper management with estimating waste PPE generation. In this study, the estimation of generation amount of waste PPE in consideration of lifestyle and medical practice is proposed by quantitative forecasting technique. In the quantitative forecasting technique, the generation source of waste PPE consisted of household and test/treatment of COVID-19. For case study in Korea, the amount of waste PPE generated from household is evaluated by applying the quantitative forecasting technique reflecting the population and measures in lifestyle due to COVID-19. Also, the estimated amount of waste PPE generation from test and treatment of COVID-19 was evaluated to have a meaningful reliability compared with other observed values. This quantitative forecasting technique can estimate the amount of waste PPE generation related to COVID-19 and develop safe management measures for waste PPE in many other countries by modifying country-specific lifestyles and medical practices.

Tropical cyclones (TCs) over the North Indian Ocean (NIO) are closely related to Asian summer monsoon activities and have a great impact on the precipitation in the Tibetan Plateau, southwestern China, and even the middle and lower reaches of the Yangtze River. In this paper, the research progress on the impacting mechanisms of NIO TCs on the weather in China and associated forecasting techniques is synthesized and reviewed, including characteristics of the NIO TC activity, its variability under climate change, related precipitation mechanism, and associated forecasting techniques. On this basis, the limitations and deficiencies in previous research on the physical mechanisms and forecasting techniques of NIO TCs affecting the weather in China are elucidated and the directions for future investigations are discussed.

This paper examines the principal trends of the government regulation of the milk-producing industry. It focuses on the rationale of those trends for improving the government regulation of dairy industry parameters and the development of effective methods for their implementation in the context of transformation into the digital economy. The study explores theoretical positions, approaches, and principles of the government regulation of digitalization of the dairy industry. It also identifies the essence and forms of this regulation. The researchers developed an economic and mathematical model of the relationship between the dairy industry parameters through a multi-level chain of indirect parameter relationship. The researchers also worked out the methodological foundations for modeling the dairy industry using digital technologies. A reverse forecasting technique was developed to estimate the necessary volume of the government support required to achieve dairy industry target indicators at any level of regulation. The model was tested with various scenarios for forecasting the results of the government regulation of the dairy industry in order to achieve the target criteria.

Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266.

The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O $ _{3} $data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O $ _{3} $forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi-step-ahead manner.

Accurate river streamflow forecasts are a vital tool in the fields of water security, flood preparation and agriculture, as well as in industry more generally. Traditional physics-based models used to produce streamflow forecasts have become increasingly sophisticated, with forecasts improving accordingly. However, the development of such models is often bound by two soft limits: empiricism – many physical relationships are represented empirical formulae; and data sparsity – long time series of observational data are often required for the calibration of these models. Artificial neural networks have previously been shown to be highly effective at simulating non-linear systems where knowledge of the underlying physical relationships is incomplete. However, they also suffer from issues related to data sparsity. Recently, hybrid forecasting systems, which combine the traditional physics-based approach with statistical forecasting techniques, have been investigated for use in hydrological applications. In this study, we test the efficacy of a type of neural network, the long short-term memory (LSTM), at predicting streamflow at 10 river gauge stations across various climatic regions of the western United States. The LSTM is trained on the catchment-mean meteorological and hydrological variables from the ERA5 and Global Flood Awareness System (GloFAS)–ERA5 reanalyses as well as historical streamflow observations. The performance of these hybrid forecasts is evaluated and compared with the performance of both raw and bias-corrected output from the Copernicus Emergency Management Service (CEMS) physics-based GloFAS. Two periods are considered, a testing phase (June 2019 to June 2020), during which the models were fed with ERA5 data to investigate how well they simulated streamflow at the 10 stations, and an operational phase (September 2020 to October 2021), during which the models were fed forecast variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to investigate how well they could predict streamflow at lead times of up to 10 d. Implications and potential improvements to this work are discussed. In summary, this is the first time an LSTM has been used in a hybrid system to create a medium-range streamflow forecast, and in beating established physics-based models, shows promise for the future of neural networks in hydrological forecasting.

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.