Wind energy forecasting with neural networks: a literature review

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version

A neural ordinary differential equations based approach for demand forecasting within power grid digital twins

Over the past few years, deep learning (DL) based electricity demand forecasting has received considerable attention amongst mathematicians, engineers and data scientists working within the smart grid domain. To this end, deep learning architectures such as deep neural networks (DNN), deep belief networks (DBN) and recurrent neural networks (RNN) have been successfully applied to forecast the generation and consumption of a wide range of energy vectors. In this work, we show preliminary results for a residential load demand forecasting solution which is realized within the framework of power grid digital twin. To this end, a novel class of deep neural networks is adopted wherein the output of the network is efficiently computed via a black-box ordinary differential equation (ODE) solver. We introduce the readers to the main concepts behind this method followed by a real-world, data driven computational benchmark test case designed to study the numerical effectiveness of the proposed approach. Initial results suggest that the ODE based solutions yield acceptable levels of accuracy for wide range of prediction horizons. We conclude that the method could prove as a valuable tool to develop forecasting models within an electrical digital twin (EDT) framework, where, in addition to accurate prediction models, a time horizon independent, computationally scalable and compact model is often desired.This research that contributed to this paper was funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and Construction (CSIC) and Centre for Digital Built Britain (CDBB) at the University of Cambridge

Prediction of home energy consumption based on gradient boosting regression tree

Abstract Energy consumption prediction of buildings has drawn attention in the related literature since it is very complex and affected by various factors. Hence, a challenging work is accurately estimating the energy consumption of buildings and improving its efficiency. Therefore, effective energy management and energy consumption forecasting are now becoming very important in advocating energy conservation. Many researchers work on saving energy and increasing the utilization rate of energy. Prior works about the energy consumption prediction combine software and hardware to provide reasonable suggestions for users based on the analyzed results. In this paper, an innovative energy consumption prediction model is established to simulate and predict the electrical energy consumption of buildings. In the proposed model, the energy consumption data is more accurately predicted by using the gradient boosting regression tree algorithm. By comparing the performance index Root Mean Square Error of different prediction models through experiments it is shown that the proposed model obtains lower values on different testing data. More detailed comparison with other existing models through experiments show that the proposed prediction model is superior to other models in energy consumption prediction

Short-Term Energy Demand Forecast in Hotels Using Hybrid Intelligent Modeling

This paper is the extension of the conference paper: Casteleiro-Roca, J.-L.; Gómez-González, J.F.; Calvo-Rolle, J.L.; Jove, E.; Quintián, H.; Acosta Martín, J.F.; Gonzalez Perez, S.; Gonzalez Diaz, B.; Calero-Garcia, F. and Méndez-Perez, J.A. Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model. In Proceedings of the 13th International Conference, Hybrid Artificial Intelligent Systems (HAIS), Oviedo, Spain, 20–22 June 2018.[Abstract] The hotel industry is an important energy consumer that needs efficient energy management methods to guarantee its performance and sustainability. The new role of hotels as prosumers increases the difficulty in the design of these methods. Also, the scenery is more complex as renewable energy systems are present in the hotel energy mix. The performance of energy management systems greatly depends on the use of reliable predictions for energy load. This paper presents a new methodology to predict energy load in a hotel based on intelligent techniques. The model proposed is based on a hybrid intelligent topology implemented with a combination of clustering techniques and intelligent regression methods (Artificial Neural Network and Support Vector Regression). The model includes its own energy demand information, occupancy rate, and temperature as inputs. The validation was done using real hotel data and compared with time-series models. Forecasts obtained were satisfactory, showing a promising potential for its use in energy management systems in hotel resortsFundación CajaCanarias; grant number PR70575

Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

open access articleShort-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 minute time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases

Short Term Load Calculating for Smart Meter Data using Weighted KNN for Classification & PredictionÂ

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