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.

The Unmet Medical Demand among China’s Urban Residents

Our work aimed to build a reasonable proxy for unmet medical demands of China’s urban residents. We combined health demand modeling and stochastic frontier analysis to produce a frontier medical demand function, which allowed us to disentangle unmet medical demands from the unobservable effects. We estimated unmet medical demands by using China’s provincial dataset that covered 2005–2018. Our estimates showed that unmet medical demand at the national level was 12.6% in 2018, and regions with high medical prices confronted more unmet medical demands than regions with moderate or low medical prices during 2005–2018. Furthermore, medical prices and education were the main factors that affected unmet medical demand; therefore, policy making should pay more attention to reducing medical costs and promoting health education.

Analytical Framework for Evaluating Potential Truck Parking Locations

As the number of trucks on the road continues to increase, mandatory rest periods combined with a decreasing number of parking spaces and amenities geared towards truck drivers have created a paradoxical yet often overlooked issue of truck parking shortage. Especially within the urbanized landscape of New Jersey, truck stops are rarely considered as the highest and best use form of development and those that exist are often expensive to operate. Most of the existing research on this issue has focused on parking demand modeling or applications of the intelligent transportation system technology to improve the use of existing truck stops. Nonetheless, limited previous research has focused on expanding truck parking capacity. This study develops a methodological framework for evaluating some of the important social, economic, and environmental factors when planning the development of a new truck parking facility. With an example application to the State of New Jersey, this study presents a step-by-step analytical process to help prioritize potential truck parking locations.

Electricity demand modeling techniques for hybrid solar PV system

In recent period, electricity need is increasing because of automatic control systems in developing modern societies. So it is necessary to estimate the consumption and needs of a particular sector to match the generation and demand for whole society. This task is performed by the modeling of electricity demand, in which many tools/plans and policies are involved. According to which, tariffs are made to benefit the society as well as energy suppliers. Electricity demand modeling is also needful when any of the generation plant is going to be installed, especially in the case of solar PV plant, in which number of panels, area, and balance of system completely depends upon the electricity demand. Hence in this work, modeling is proposed for electricity demand after analyzing various sectors. After the proper energy audit in initial stage, hybrid generation system (Thermal/Solar PV/DG/Batteries) will be modeled to match the demand in peak hours using metaheuristics optimization techniques. Low carbon emission and energy storage are the key features of power generation using solar PV system, which are very beneficial for any state of India.