The Daily and Hourly Energy Consumption and Load Forecasting Using Artificial Neural Network Method: A Case Study Using a Set of 93 Households in Portugal

Rodrigues, Filipe Martins; Cardeira, Carlos; Calado, João Manuel Ferreira

doi:10.1016/j.egypro.2014.12.383

Cited by 139 publications

(84 citation statements)

References 18 publications

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“…Since the identification of input variables is very crucial and requires a systematic approach such as sensitivity analysis. Rodrigues et al [29] proposed a Levenberg-Marquardt algorithm based ANN for short-term electricity consumption for 96 buildings. The proposed method predicted daily electricity consumption with 18.1% means average percentage error.…”

Section: Introductionmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

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Abstract:This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers' employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

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Section: Introductionmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

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“…This approach includes the transfer of some units for the periods when demand is lower and energy storage to greater supply (production). Through knowledge of the profile curve to power consumption of each household it is possible to find a model for optimizing the electrical energy used [2].…”

Section: Introductionmentioning

confidence: 99%

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

Rodrigues¹,

Cardeira²,

João³

et al. 2016

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Abstract. 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.

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“…So far, many ANN models have been published in the literature and showed adequate forecasting results. In [14], an ANN-based hourly load forecasting model was proposed for a household application. In [15], electricity price is considered as an input parameter for load forecasting in real-time pricing markets.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Demand Side Short Term Load Forecasting

2016

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Abstract:In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN)-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer's electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN), a double seasonal Holt-Winters (DSHW) model and the autoregressive integrated moving average (ARIMA). The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

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The Daily and Hourly Energy Consumption and Load Forecasting Using Artificial Neural Network Method: A Case Study Using a Set of 93 Households in Portugal

Cited by 139 publications

References 18 publications

A Smart Forecasting Approach to District Energy Management

A Smart Forecasting Approach to District Energy Management

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

Deep Neural Network Based Demand Side Short Term Load Forecasting

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