Energy Household Forecast with ANN for Demand Response and Demand Side Management

This paper presents a short term load forecasting with artificial neural networks. Despite the great imprevisibility, it is possible to forecast the electricity consumption of a household with some accuracy, similarly to that the electricity utilities can do to an agglomerate of households. Nowadays, in an existing electric grid, it is important to understand and forecast household daily or hourly consumption with a reliable model for electric energy consumption and load profile. Demand response programs required this information to adequate the profile of energy load diagram to generation. In the short term load forecasting model, artificial neural networks were used, with a consumption records database. The results show that the artificial neural networks approach provides a reliable model for forecasting household electric energy consumption and load profile. To do so and using smart devices such as cyber-physical systems monitoring, gathering and computing in real time a database with weekdays and weekend, can improve forecasts results for the next hours, a strong tool for Demand Response and Demand Side Management

Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting

Forecasting electricity demand in households using MOGA-designed artificial neural networks

The prediction of electricity demand plays an essential role in the building environment. It strongly contributes to making the building more energy-efficient, having the potential to increase both thermal and visual comfort of the occupants, while reducing energy consumption, by allowing the use of model predictive control. The present article focuses on the use of computational intelligence methods for prediction of the power consumption of a case study residential building, during a horizon of 12 hours. Two exogeneous variables (ambient temperature and day code) are used in the NARX model Two different time steps were considered in the simulations, as well as constrained and unconstrained model design. The study concluded that the smaller timestep and the constrained model design obtain the best power demand prediction performance. The results obtained compare very favourably with similar approaches in the literature Copyright (C) 2020 The Authors.UIDB/50022/2020, 01/SAICT/2018info:eu-repo/semantics/publishedVersio

A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market

Due to recent technical improvements, the smart grid has become a feasible platform for electricity market participants to successfully regulate their bidding process based on demand-side management (DSM) perspectives. At this level, practical design, implementation, and assessment of numerous demand response mechanisms and robust short-term price forecasting development in day-ahead transactions are all critical. The accuracy and effectiveness of the day-ahead price forecasting process are crucial concerns in a deregulated market. In this market, the reason for low accuracy is the limitation of electricity generation compared to the electricity demand variations. Hence, this study proposes a suitable technique for forecasting electricity prices using a multi-head self-attention and Convolutional Neural networks (CNN) based approach. Further, this study develops a feature selection technique using mutual information (MI) and neural networks (NN) to choose suitable input variable subsets significantly affecting electricity price predictions simultaneously. The combination of MI and NN reduces the number of input features used in the model, thereby decreasing the computational complexity of the NN. The actual data sets from the Ontario electricity market in 2020 are acquired to verify the simulation results. Finally, the simulation results proved the efficiency of the proposed method by demonstrating increased accuracy by attaining the lowest average value for MAPE and RMSE with a value of 1.75% and 0.0085, respectively, and compared to results obtained by recent computational intelligence approaches. By attaining accurate electricity price results, the significance of this study can be summed up as aiding the electricity industry's operators in administering effective energy management, efficient resource allocation, and informed decision-making.© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Structure combination of forecasting models with application in the energy sector

This dissertation proposes and implements the inclusion of model structure in combining forecasts. Empirical investigations are conducted with an emphasis on neural networks and seasonal exponential smoothing models using synthetic data and real time series, from the electricity sector. It starts with a literature review on combining forecasts and ensembles of neural networks, and highlights their use in forecasting within the energy sector. Research gaps are identified and the questions to be addressed in this research are set, thus leading to three empirical studies. The first study provides a detailed sensitivity analysis of the goodness-of-fit and forecasting performance of feed-forward neural networks on time series with different characteristics. It expands existing literature by increasing the number and variety of time series and by using graphical and statistical diagnostics to objectively judge the influence of model specification on forecasting performance. Having identified conditions for achieving stable model performance, this study facilitated the identification of suitable models for different time series characteristics, which are then useful in developing combinations (ensembles) of feed forward neural networks. The second study proposes structural combination methods based on clustering (CB) and genetic algorithms (GA) for forecasting time series. Clustering of neural networks using their parameter space is performed to identify a pool of forecasts to be combined. Three synthetic time series and two real time series (electricity demand and wind power production) were used to assess the performance of the two proposals against several benchmarks in univariate and multivariate forecasting problems. Structural combinations with GA were more competitive than those with CB for non-seasonal time series and the multivariate wind power forecasting application, whereas for the seasonal series, the CB tended to be more competitive. The third study focused on forecasting univariate time series with seasonality, by structurally combining, in separate applications, multiplicative Holt-Winters and multiplicative Holt-Winters-Taylor models. Noise addition and block swapping were applied to the original time series in order to generate structurally diverse individual models. Applications were conducted using a seasonal daily peak electricity demand time series, an hourly double-seasonal electricity demand series and a half-hourly double-seasonal electricity demand series. Structural combinations worked better for the peak electricity demand and half-hourly demand time series when model variation was induced via noise addition. For the double-seasonal hourly electricity demand, block swapping, as a means for diversity in models, resulted in better forecasts. Finally, in the last chapter of this dissertation, conclusions are drawn from this research. The contribution to the literature is assessed and a future research agenda is proposed

Studi Peramalan Beban Rata – Rata Jangka Pendek Menggunakan Metoda Autoregressive Integrated Moving Average (Arima

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast

Ensemble prediction model with expert selection for electricity price forecasting

Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets

A comparison of univariate methods for forecasting electricity demand up to a day ahead

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives