Short-term hourly load forecasting using time-series modeling with peak load estimation capability

By Nima Amjady. 2001

711 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/cd4a7f9526118d4eb4c1cb85c912abb3c8d7d5ba

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In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting (2012, methodology): “For this reason time series methods such as linear univariate model [9], ARIMA [3], Hagan and Behr [15], in combination with econometrics model [10], GIGARCH [12], GARCH [13] and hybrid models such as combination of ARIMA and GARCH using wavelet transform [28] were not considered for STMLF.”
A review of machine learning in building load prediction (2021, neutral): “…algorithms are also applied to load prediction, including Double Seasonal ARIMA [76], Analytical Hierarchy Process [77], Novel Box Jenkins method [78], Periodic Autoregression [79], Seasonal Autoregressive Integrated Moving Average with Exogenous Variables model [80], ARMAX with PSO [81], and…”
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach (2006, background): “Interestingly, for a dataset comprising PJM prices from year 2002, the ANN technique was the worst out of the five tested models! On the other hand, Amjady (2006) showed that while a multilayer perception performed worse for Spanish market data than a selection of statistical techniques (including t”
Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data (2008, methodology): “Over the years, different forecasting techniques have been developed to model the electricity load, both in the classical time series literature (Al-Hamadi & Soliman, 2004; Amjady, 2001; Bunn& Farmer, 1985a; rs. Published by Elsevier B.V.”
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, methodology): “The model takes previous load data and temperature data as input variables [79].”
Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns (2013, background): “As a general rule, in this scope clustering is commonly used to classify energy consumers [7], predict future energy demand [8,9], or detect distinguished, habitually undesired, behaviors ( i.e. , outliers) [10].”
Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption (2008, background): “Amjady (2001) [18] proposed a time series modeling for short term hourly forecast for peak loads based on auto regressive integrated moving average (ARIMA).”
Effects of data cleansing on load prediction algorithms (2013, background): “The importance of short term (up to a week ahead) forecasting increased after deregulation of the power market, allowing competition between the buying and selling parties.”
Probabilistic forecasts of the magnitude and timing of peak electricity demand (2005, background): “S. Bouwman and G. Bloemhof are with the KEMA Transmission and Distribution Consulting, 6800 ET Arnhem, The Netherlands (e-mail: Sonja.Bouwman@kema.com; Gabriel.Bloemhof@kema.com).”
Modeling Real-Time Balancing Power Market Prices Using Combined SARIMA and Markov Processes (2008, methodology): “Previously, models based on such processes have been used for load forecasts [5]–[7] and to forecast day-ahead spot market prices [8]–[12].”
Medium-term electric load forecasting using singular value decomposition (2011, methodology): “ARMA and the previously mentioned versions are used extensively in short-term load forecasting [8,9,15,16].”
Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 (2004, background): “In fact, in some load forecasting works, time series models have been explored ([1], [18]).”
Electric load forecasting methods: Tools for decision making (2009, methodology): “As mentioned in Taylor et al. (2006), univariate ARIMA-models are often used as sophisticated benchmark models in STLF. Amjady used a modified ARIMA-model to predict the hourly load demand and the daily peak loads in STFL (Amjady, 2001).”