Global vs. local models for short-term electricity demand prediction in a residential/lodging scenario
A Buonanno, M Caliano, A Pontecorvo, G Sforza… - Energies, 2022 - mdpi.com
Electrical load forecasting has a fundamental role in the decision-making process of energy
system operators. When many users are connected to the grid, high-performance …
system operators. When many users are connected to the grid, high-performance …
Machine-learning based prediction of multiple types of network traffic
Prior knowledge regarding approximated future traffic requirements allows adjusting
suitable network parameters to improve the network's performance. To this end, various …
suitable network parameters to improve the network's performance. To this end, various …
[PDF][PDF] Prediction of water demand using artificial neural networks models and statistical model
M Awad, M Zaid-Alkelani - International Journal of Intelligent Systems …, 2019 - academia.edu
The prediction of future water demand will help water distribution companies and
government to plan the distribution process of water, which impacts on sustainable …
government to plan the distribution process of water, which impacts on sustainable …
Machine Learning Applications for Renewable-Based Energy Systems
G Graditi, A Buonanno, M Caliano, M Di Somma… - Advances in Artificial …, 2023 - Springer
Abstract Machine learning is becoming a fundamental tool in current energy systems. It
helps to obtain accurate predictions of the variable renewable energy (VRE) generation …
helps to obtain accurate predictions of the variable renewable energy (VRE) generation …
Adaptive workload prediction for proactive auto scaling in PaaS systems
RS Shariffdeen, D Munasinghe… - … on Cloud Computing …, 2016 - ieeexplore.ieee.org
Elasticity is a key feature of cloud computing where resources are allocated and released
according to user demands. Reactive auto scaling, in which the scaling actions take place …
according to user demands. Reactive auto scaling, in which the scaling actions take place …
Tree-based methods for clustering time series using domain-relevant attributes
We propose two methods for time-series clustering that capture temporal information (trend,
seasonality, autocorrelation) and domain-relevant cross-sectional attributes. The methods …
seasonality, autocorrelation) and domain-relevant cross-sectional attributes. The methods …
An intelligent approach to demand forecasting
NCD Adhikari, N Domakonda, C Chandan… - … on Computer Networks …, 2019 - Springer
Demand Forecasting, undeniably, is the single most important component of any
organizations Supply Chain. It determines the estimated demand for the future and sets the …
organizations Supply Chain. It determines the estimated demand for the future and sets the …
Modular neural networks for time series prediction using type-1 fuzzy logic integration
In this paper, a new method to perform the times series prediction using modular neural
networks with type-1 fuzzy logic integration is proposed. The proposed method consists in …
networks with type-1 fuzzy logic integration is proposed. The proposed method consists in …
Time series forecasting using fuzzy techniques
T Afanasieva, N Yarushkina, M Toneryan… - 2015 Conference of …, 2015 - atlantis-press.com
The aim of this contribution is to show the opportunities of applying of fuzzy time series
models to predict multiple heterogeneous time series, given at International Time Series …
models to predict multiple heterogeneous time series, given at International Time Series …
Using real-world store data for foot traffic forecasting
S Abrishami, P Kumar - … Conference on Big Data (Big Data), 2018 - ieeexplore.ieee.org
Time series forecasting is a fundamental task in machine learning and data mining. It is an
active area of research, especially in applications that have direct impact on the real-world …
active area of research, especially in applications that have direct impact on the real-world …