Short term power load forecasting using Deep Neural Networks

Accurate load forecasting greatly influences the planning processes undertaken in operation centres of energy providers that relate to the actual electricity generation, distribution, system maintenance as well as electricity pricing. This paper exploits the applicability of and compares the performance of the Feed-forward Deep Neural Network (FF-DNN) and Recurrent Deep Neural Network (R-DNN) models on the basis of accuracy and computational performance in the context of time-wise short term forecast of electricity load. The herein proposed method is evaluated over real datasets gathered in a period of 4 years and provides forecasts on the basis of days and weeks ahead. The contribution behind this work lies with the utilisation of a time-frequency (TF) feature selection procedure from the actual “raw” dataset that aids the regression procedure initiated by the aforementioned DNNs. We show that the introduced scheme may adequately learn hidden patterns and accurately determine the short-term load consumption forecast by utilising a range of heterogeneous sources of input that relate not necessarily with the measurement of load itself but also with other parameters such as the effects of weather, time, holidays, lagged electricity load and its distribution over the period. Overall, our generated outcomes reveal that the synergistic use of TF feature analysis with DNNs enables to obtain higher accuracy by capturing dominant factors that affect electricity consumption patterns and can surely contribute significantly in next generation power systems and the recently introduced SmartGrid

Short Term Load Forecasting Using TabNet: A Comparative Study with Traditional State-of-the-Art Regression Models

Electric load forecasting is becoming increasingly challenging due to the growing penetration of decentralised energy generation and power-electronics based loads such as heat pumps and electric vehicles, which adds to a transition to more variable work patterns (accentuated by the COVID-19 pandemic in 2020). In this paper, three different Machine Leaning models are analysed to predict the energy load one week ahead for a period of time including the COVID-19 pandemic. It is shown that, by using the recently proposed TabNet model architecture, it is possible to achieve an accuracy comparable to more traditional approaches based on gradient boosting and artificial neural networks without the need of performing complex feature engineering

Long-Term Electricity Load Forecasting Based On Cascade Forward Backpropagation Neural Network

Nowadays, the Electrical System has an important role in all sectors of life. Electricity has a strategic role. Accuracy and reliability in electricity load forecasting is a great key that can help electricity companies in supplying electricity efficiency, hence, reducing wasted energy. In addition, electricity load forecasting can also help electricity companies to determine the purchase price and power generation. Long-term forecasting is a method of forecasting with a span of more than one year. The historical data will be a reference in solving the problems. This research propose the concept of cascade forward backpropagation for long-term load forecasting. The advantage of this concept is that it can accommodate non-linear conditions without ignoring the linear conditions. This study compared the results of the original data, Feed Forward Backpropagation Neural Network (FFBNN) and Cascade Forward Backpropagation Neural Network (CFBNN). The results were measured by comparing Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE)

A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem

AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING

This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems

Short Term Load Forecasting Using Recurrent and Feedforward Neural Networks

Accurate load forecasting greatly influences energy production planning. If the demand forecast is inaccurate this could lead to blackouts or waste of precious energy. This paper compares many innovative networks on the basis of accuracy. The first is a feedforward neural network (FFNN). Next we look at different models of Recurrent Neural Networks (RNN) specifically long short term Memory (LSTM). Finally we explore combining the two approaches into a hybrid network. We will be predicting load with an hourly granularity also known as short term load forecasting (STLF). We will be applying these approaches to real world data sets from www.eia.gov over a period of about 4 years. Our approach will focus on the integration of historical time features from the last hour, day, month, etc. with the inclusion of RNN methods. We show that the included time features reduce the overall error and increase generalizability. We combine this with features such as weather, cyclical time features, cloud cover, and the day of the year to further reduce the error. We will then compare the approaches to reveal that the correct handling of time features significantly improves the model by learning hidden features

PRONÓSTICO DE DEMANDA DE ENERGÍA ELÉCTRICA USANDO PROCESOS GAUSSIANOS: UN ANÁLISIS COMPARATIVO: Short-Term Load Demand Forecasting using Gaussian Processes: A Comparative Analysis

Abstract—Load demand forecasting is an essential component for planning power systems, and it is an invaluable tool to grid operators or customers. Many methods have been proposed to provide reliable estimates of electric load demand, but few methods can address the problem of predicting energy demand from a probabilistic point of view. One of them is the Gaussian processes (GP) that considering an adequate covariance function are suitable tools to carry out this load forecasting task. In this article, we show how to use Gaussian processes to predict elec- trical energy demand. Additionally, we thoroughly test various covariance functions and provide a new one. The performance of the proposed methodology was tested on two real data sets, showing that GPs are competitive alternatives for short-term load demand forecasting compared to other state-of-the-art method