The transition to a cleaner energy mix, essential for achieving net-zero greenhouse gas emissions by 2050, will significantly increase demand for metals critical to renewable energy technologies. Energy Transition Metals (ETMs), including copper, lithium, nickel, cobalt, and rare earth elements,...

Accurate forecasting of solar PV generation is critical for integrating renewable energy into power systems. This paper presents a multivariate probabilistic forecasting model that addresses the challenges posed by imbalanced data resulting from day and night-time periods in solar photovoltaic...

This paper presents PyPSA-GB, a dataset and model of Great Britain’s (GB) power system encompassing historical years and the future energy scenarios developed by National Grid. It is the first fully open-source model implementation of the future GB power system with high spatial and temporal...

Energy-outlook from past to future specific years has become essential in energy-economy. Malaysia is a member of ASEAN (Association of South-east Asian Nations), and ASEAN is increasing the use of renewable energy by 2050 to reduce carbon dioxide (CO2) emissions. Coal is Malaysia's primary...

Behavioral changes due to digitalization, such as telework, shifted energy demand, especially during COVID-19. Behavioral changes are often overlooked in macro-demand forecasts. This study forecasts macro-energy use to 2030 using American Time Use Survey and national energy data from 2003-2019....

This study introduces a novel approach to predicting global oil demand by integrating machine learning (ML) techniques to forecast consumption across seven refined oil products and seven key regions. By aggregating these forecasts, we offer a comprehensive view of global demand trends. The paper...

This study is the first to explore whether the well-known relationship between the clean energy sector, oil prices, and technology stocks can be leveraged to enhance the accuracy of realized volatility forecasts for individual clean energy sub-sectors. Based on intraday data and various...

A circular economy requires managing and reducing the environmental and social impacts of products and materials across their lifecycle from production to end-of-life, including clean energy technologies. The unprecedented growth in wind turbine (WT) deployment in Australia as a clean energy...

Purpose: Sustaining healthcare operations without adequate energy capacity creates significant challenges, especially during periods of constrained energy supply. This research develops a clinical and non-clinical activity-based hospital energy model for electrical load prioritization during...

The electric power industry is the key to achieving the goals of carbon peak and neutrality. Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation policies and the early achievement of carbon reduction targets. This study...

Global climate changes urge prompt energy transition for less carbon emissions, from traditional fossil fuels to renewable and sustainable clean energy. However, in reality, the world's energy majority cannot make U-turn immediately to renewables or clean energy due to the immature technology...

Wave energy research is a reality in many parts of the world. It is essential for any wave power plant at the commercializing stage to accurately model and forecast its production to ensure efficient trading in the daily wholesale electricity market. Altogether, a reliable forecast is a required...

Many behavioral responses to climate change are carbon-intensive, raising concerns that adaptation may cause additional warming. The sign and magnitude of this feedback depend on how increased emissions from cooling balance against reduced emissions from heating across space and time. We present...

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique weekly and monthly electricity consumption data for the second-largest German state, the Free State of Bavaria, we conduct a pseudo out-of-sample forecasting experiment for...

Statistical learning models are proposed for the prediction of the probability of a spike in the electricity DART (day-ahead minus real-time price) spread. Assessing the likelihood of DART spikes is of paramount importance for virtual bidders, among others. The model's performance is evaluated...

In recent years, the international community has been increasing its efforts to reduce the human footprint on air pollution and global warming. Total CO2 emissions are a key component of global emissions, and as such, they are closely monitored by national and supranational entities. This study...

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique weekly and monthly electricity consumption data for the second-largest German state, the Free State of Bavaria, we conduct a pseudo out-of-sample forecasting experiment for...

In General Relativity Theory, the simplest explanation for dark energy is the cosmological constant $\Lambda$. However, the cosmological constant $\Lambda$ facing a sensitive problem called fine-tunning problem. In the present work, we follow a different approach where the gravitational sector...

Modelling and forecasting electricity consumption (EC) help industry managers make better strategic decisions. In this study, a hybrid approach for predicting EC is proposed which first EC is decomposed into approximate and detail parts based on wavelet transfer (WT). Next, the modeling of...

Electricity consumption is China's main energy consumption and an important indicator of economic performance. Forecasting electricity consumption plays an increasingly important role in economic development. In order to solve this problem, a fractional order discrete Verhulst model was proposed...

The last decade has seen significant changes in the Polish economy and industry, which resulted in substantial changes in the power demand. This paper investigates and analyses the trends of changes in power demand in the Polish Power System (PPS). Based on the historical data of the PPS, the...

Accurate forecasting results are crucial for increasing energy efficiency and lowering energy consumption in wind energy. Big data and artificial intelligence (AI) have great potential in wind energy forecasting. Although the literature on this subject is extensive, it lacks a comprehensive...

Pakistan has been in severe energy crises since the year 2004. Major reasons behind energy crises are lack of use of modeling tools in power planning and policy development, dependence on imported energy sources and poor governance. In this paper, electricity demand forecasting for Pakistan up...