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Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Sina Shabani, Antonio Candelieri, Francesco Archetti and Gholamreza Naser

This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand dat... see more

short-term water demand forecasting | multi-scale modeling | gene expression programming | clustering | average mutual information

Water , 2018

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A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Francesca Gagliardi, Stefano Alvisi, Zoran Kapelan, Marco Franchini

This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals cover... see more

water demand | forecasting | Markov chain | stochastic

Water , 2017

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Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

Antonio Candelieri

This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data)... see more

time-series clustering | support vector regression | water demand forecasting | anomaly detection

Water , 2017

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A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

Elena Pacchin, Stefano Alvisi, Marco Franchini

In this article, a model for forecasting water demands over a 24-h time window using solely a pair of coefficients whose value is updated at every forecasting step is presented. The first coefficient expresses the ratio between the average water demand ov... see more

water demand | forecast | moving window

Water , 2017

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Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks

Abdullah Alsumaiei

Freshwater supply is a major challenge in regions with limited water resources and extremely arid climatic conditions. The objective of this study is to model the monthly water demand in the State of Kuwait using the NARX neural network approach. The coun... see more

Journal of Engineering Research (JER) , 2021

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Evaluating the Impact of Meteorological Factors on Water Demand in the Las Vegas Valley Using Time-Series Analysis: 1990–2014

Patcha Huntra and Tim C. Keener

Many factors impact a city’s water consumption, including population distribution, average household income, water prices, water conservation programs, and climate. Of these, however, meteorological effects are considered to be the primary determinants of... see more

ISPRS International Journal of Geo-Information , 2017

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Short-Term Load Forecasting Using Artificial Neural Network in Indonesia Sylvia Jane Annatje Sumarauw(1) (1) Universitas Negeri Manado () Corresponding Author

Short-term Load Forecast (STLF) is a load forecasting that is very important to study because it determines the operating pattern of the electrical system. Forecasting errors, both positive and negative, result in considerable losses because operating cos... see more

Short-Term Load Forecast | Artificial Neural Network | Forecasting Performance | State Electricity Company

ILKOM Jurnal Ilmiah , 2023

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Hierarchical Clustering and Deep Learning for Short-Term Load Forecasting with Influenced Factors

Rio Indralaksono, M. Abdul Wakhid, Novemi Uki A, Galih Hendra Wibowo, M. Abdillah, Agus Budi Rahardjo, Diana Purwitasari

Stable and reliable electricity is one of the essential things that must be maintained by the transmission system operator (TSO). That can be achieved when the TSO is able to set the balance between demand and production. To maintain the balance between p... see more

forecasting | electricity | load | Long Short-term Memory | Analytical Hierarchical Clustering

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) , 2022

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Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting

Accurate short-term electric load forecasting is significant for the smart grid. It can reduce electric power consumption and ensure the balance between power supply and demand. In this paper, the Stacked Denoising Auto-Encoder (SDAE) is adopted for short... see more

short-term load forecasting | deep learning | stacked denoising auto-encoder neural network

Energies , 2019

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