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  • Hourly electricity demand forecasting using Fourier analysis with . . .
    As examples of general forecast models, in Ref [6], the authors use weather scenarios to forecast short-term electricity demand for 1–10 days ahead In Ref [7, 8] the researchers use statistical modelling to analyse the influence of temperature on load forecasting in Italy, both at the national and regional level, using historical load data In Ref [9], different load profile patterns are
  • A Recent Review Article on Demand Forecasting - ResearchGate
    Solutions of short-term forecasting problems provide credible predictions for energy demand Calculations for medium-term forecasts that extend beyond 6-months are also very promising
  • AI-Based Demand Forecasting Models: A Systematic Literature Review
    literature, we find the forecast horizon, which can be hourly, daily, weekly, monthly, and annually [13] As well as load forecasting which can be short-term (from one hour to one week), medium (from a week to a year), and long-term forecasts (more than a year) [14] 2 3 Demand forecasting methods Demand forecasting involves predicting the future
  • Electricity demand forecasting methodologies and applications: a review . . .
    Electricity demand forecasting has emerged as a critical area of research in recent times, driven by the necessity for accurate predictions of future load requirements Such predictions are essential for effectively operating and planning electric power systems Various forecasting methodologies and approaches have been employed to estimate electricity demand, emphasizing the need for
  • AI System for Short Term Prediction of Hourly Electricity Demand - Springer
    Historical data is typically used in building forecasting systems In electric energy industry prices are forecasted on hourly basis [] and in long-term horizon [4, 10, 19] show that in last 10 years more than 170 papers were published on that subject Probabilistic energy forecasting [] and short-term load forecasts are important for production system and energy markets []
  • Seasonal forecasting of the hourly electricity demand applying machine . . .
    The purpose of this paper is to suggest short-term Seasonal forecasting for hourly electricity demand in the New England Control Area (ISO-NE-CA) Precision improvements are also considered when
  • Review article
    LF can be divided into two main categories based on the time horizon of the data Short-term load forecasting (STLF) involves predicting electricity demand in the relatively short-term, typically a few minutes, hours, or days ahead, while long-term LF relates to predictions up to 20 years ahead [2] LF minimises the risk of energy supply and
  • Deep Learning Techniques for Demand Forecasting: : Review and Future . . .
    Bedi, J , Toshniwal, D (2019) Deep learning framework to forecast electricity demand Applied Energy, 238, 1312–1326 Predictive models for forecasting hourly urban water demand Journal of Hydrology (Amsterdam), 387(1–2), 141–150 Crossref Google Scholar Deep Learning Techniques for Demand Forecasting: Review and Future
  • Comparative study of continuous hourly energy consumption forecasting . . .
    Based on this, the objective of this study is to determine, through continuous hourly electricity consumption forecasting strategies, the amount of data needed to achieve an accurate forecast The
  • An ensemble of artificial neural network models to forecast hourly . . .
    The case study comprised data from January 2007 to June 2016, and used a forecasting horizon of four hours Ensemble method approaches are also adopted For example, Wu et al , proposed a method combining an adaptive network-based fuzzy inference system and an Elman Neural Network for the short-term electricity demand forecasting The model





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