Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

Modeling Energy Demand—A Systematic Literature Review

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.

Medical waste management during coronavirus disease 2019 pandemic at the city level

Coronavirus disease 2019 brings about the economic damage and loss of life. Thus, demand of personal protective equipment continues to increase, consequently an increase in infectious equipment pollution. Most of these wastes threaten the environment and increase the spread of diseases. This paper provides a research hypothesis whether effective medical waste management would prevent the possible impacts of coronavirus disease 2019-related waste issues on environment at the city level. To confirm this hypothesis, installation of waste treatment centre is addressed. Then, by incorporating uncertain waste generation amounts utilizing Jimenez method, a pickup routing is addressed to decide the pickup routes between the waste treatment centre and residential area. This study is first to assign the optimistic, realistic and pessimistic scenarios of the uncertain waste generation using time series analysis method and waste generation formulation. Also, L-type matrix is used to define, assess and prioritize the environmental and operational risks on waste generation formulation and to provide risk reaction strategies. Practicality of these approaches is illustrated in the case of Turkey. The computational results reveal the effectiveness of the integrated method, which ensures practical and theoretical insights controlling the waste generation to prevent virus propagation for health authorities.

Forecasting and Assessment of the Energy Security Risk in Fuzzy Environment

Energy security is of key importance for states and international organizations. An important issue in energy security is the assessment of current and future energy security methods. While the assessment of the current methods is relatively easy, since it is based on recent information, the assessment of the future methods is burdened with uncertainty and is therefore much more difficult. Therefore, the aim of the article is to develop a new approach for assessing current and future energy security issues based on a complex security index, supported by the computationally transparent fuzzy multi-criteria decision analysis (MCDA) method. The use of the fuzzy MCDA methods allows one to capture the uncertainty of assessments and forecasts, and the forecasts themselves were based on the Holt’s method; the international energy security risk index (IESRI) was used as the source of the data to generate the forecasts. The research compared two data sources for forecasts (IESRI categories and metrics) and two methods of forecast fuzzification. As a result, the forecasted assessments and rankings of energy security for the 2020–2030 period were obtained. On the basis of these forecasts, general trends shaping energy security were also indicated.

Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process

Distribution companies use time series to predict electricity consumption. Forecasting techniques based on statistical models or artificial intelligence are used. Reliable forecasts are required for efficient grid management in terms of both supply and capacity. One common underlying feature of most demand–related time series is a strong seasonality component. However, in some cases, the electricity demanded by a process presents an irregular seasonal component, which prevents any type of forecast. In this article, we evaluated forecasting methods based on the use of multiple seasonal models: ARIMA, Holt-Winters models with discrete interval moving seasonality, and neural networks. The models are explained and applied to a real situation, for a node that feeds a galvanizing factory. The zinc hot-dip galvanizing process is widely used in the automotive sector for the protection of steel against corrosion. It requires enormous energy consumption, and this has a direct impact on companies’ income statements. In addition, it significantly affects energy distribution companies, as these companies must provide for instant consumption in their supply lines to ensure sufficient energy is distributed both for the process and for all the other consumers. The results show a substantial increase in the accuracy of predictions, which contributes to a better management of the electrical distribution.

Forecasting of Electrical Generation Using Prophet and Multiple Seasonality of Holt–Winters Models: A Case Study of Kuwait

Electrical generation forecasting is essential for management and policymakers due to the crucial data provided for resource planning. This research employs the Prophet model with single and multiple regressors to forecast the electricity generation in Kuwait from 2020 to 2030. In addition, multiple seasonality Holt–Winters models were utilized as a benchmark for comparative analysis. The accuracy, generalization, and robustness of the models were assessed based on different statistical performance metrics. The triple seasonality Holt–Winters model achieved superior performance compared with the other models with R2 = 0.9899 and MAPE = 1.76%, followed by the double seasonality Holt–Winters model with R2 = 0.9893 and MAPE = 1.83%. Moreover, the Prophet model with multiple regressors was the third-best performing model with R2 = 0.9743 and MAPE = 2.77%. The forecasted annual generation in the year 2030 resulted in 92,535,555 kWh according to the best performing model. The study provides an outlook on the medium- and long-term electrical generation. Furthermore, the impact of fuel cost is investigated based on the five forecasting models to provide an insight for Kuwait’s policymakers.