Related papers: Reconstruction of Long-Term Historical Demand Data
Growth of intermittent renewable energy and climate change make it increasingly difficult to manage electricity demand variability. Centralized storage can help but is costly. An alternative is to shift demand. Cooling and heating demands…
The future energy system will largely depend on volatile renewable energy sources and temperature-dependent loads, which makes the weather a central influencing factor. This article presents a novel approach for simulating weather scenarios…
The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the…
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between renewal processes, and a collection of current models used…
Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time,…
The long-term forecasting of electricity demand has been a prevalent research topic, primarily because of its economic and strategic relevance. Several machine learning as well as deep learning techniques have been developed in parallel…
Decisions related to electric power systems planning and operations rely on assumptions and insights informed by historic weather data and records of past performance. Evolving climate trends are, however, changing the energy use patterns…
Electricity is difficult to store, except at prohibitive cost, and therefore the balance between generation and load must be maintained at all times. Electricity is traditionally managed by anticipating demand and intermittent production…
Sustainable energy systems require flexible elements to balance the variability of renewable energy sources. Demand response aims to adapt the demand to the variable generation, in particular by shifting the load in time. In this article,…
Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate…
We explore the crucial interplay between climate change and power system planning, highlighting the urgent need to systematically integrate climate information into energy system studies. Climate change impacts the energy sector on multiple…
The integration of solar power has been increasing as the green energy transition rolls out. The penetration of solar power challenges the grid stability and energy scheduling, due to its intermittent energy generation. Accurate and near…
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or…
The high temporal variability of wind power generation represents a major challenge for the realization of a sustainable energy supply. Large backup and storage facilities are necessary to secure the supply in periods of low renewable…
Accurate renewable energy production forecasting has become a priority as the share of intermittent energy sources on the grid increases. Recent work has shown that convolutional deep learning models can successfully be applied to forecast…
The increasing severity of climate change necessitates an urgent transition to renewable energy sources, making the large-scale adoption of wind energy crucial for mitigating environmental impact. However, the inherent uncertainty of wind…
This paper introduces a generalised version of importance subsampling for time series reduction/aggregation in optimisation-based power system planning models. Recent studies indicate that reliably determining optimal electricity…
Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations, particularly from wind and solar energy resources, are witnessing a rapid boost, and, it is anticipated…
Energy demand prediction is critical for grid operators, industrial energy consumers, and service providers. Energy demand is influenced by multiple factors, including weather conditions (e.g. temperature, humidity, wind speed, solar…
The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these…