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Short-Term Electricity Load and Price Forecasting using Enhanced KNN
Abstract
In this paper, we introduced a new enhanced technique , to resolve the issue of electricity price and load forecasting. In Smart Grids (SGs) Price and load forecasting is the major issue. Framework of enhanced technique comprises of classification and feature engineering. Feature engineering comprises of feature selection and feature extraction. Decision Tree Regression (DTR) is used for feature selection. Recursive Feature Elimination (RFE) is used for feature selection which eliminates the redundancy of features. The second step of feature engineering, feature extraction, is done using Singular Value Decomposition (SVD), which reduces the dimensionality of features. Last step is to predict the load and forecast. For forecasting electricity load and price, two existing techniques, K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP), and a newly proposed technique known as Enhanced KNN (EKNN) is being used. The proposed technique outperforms than MLP and KNN in terms of accuracy. KNN is working on nonparametric method which is used for classification and regression.
... Alongside the improvement in computation capability of processors and Machine Learning (ML) and Deep Learning (DL) techniques in computer science, electrical engineering researchers find out the benefits of deploying ML and DL for demand forecasting. For example, references [10]- [12] developed K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Artificial Neural Network (ANN) as data-driven demand forecast models at New York state, Northeastern China, and Pecan street, respectively. A deep convolution neural network is presented in [13] for demand forecast in three Chinese cities. ...
... The loss function is defined in according with (10). In the training stage, the model is trained in a way in which the expected value of loss is minimized. ...
Electricity demand forecast is necessary for power systems' operation scheduling and management. However, power consumption is uncertain and depends on several factors. Moreover, since the onset of covid-19, the electricity consumption pattern went through significant changes across the globe, which made the forecasting demand more challenging. This is mainly due to the fact that pandemic-driven restrictions changed people's lifestyles and work activities. This calls for new forecasting algorithms to more effectively handle these conditions. In this paper, ensemble-based machine learning models are utilized for this task. The lockdown temporal policies are added to the feature set in order to make the model capable of correcting itself in pandemic situations and enhance data quality for the forecasting task. Several ensemble-based machine learning models are examined for the short-term country-level demand prediction model. Besides, the quantile random forest regression is implemented for a probabilistic point of view. For case studies, the models are trained for predicting Germany's country-level demand. The results indicate that ensemble models, especially boosting and bagging-boosting models, are capable of accurate country-level demand forecast. Besides, the majority of these models are robust against missing the pandemic policy data. However, utilizing the pandemic policy data as features increases the forecasting accuracy during the pandemic situation significantly. Furthermore, the probabilistic quantile regression demonstrated high accuracy for the aforementioned case study.
... Many researchers have developed predictive models to forecast electricity prices in various countries and scenarios. Some of the ML models widely used for this task include support vector machines (SVMs) [24][25][26], tree-based models [27][28][29], k-nearest neighbor (KNN) [30][31][32], shallow architectures of artificial neural networks (ANNs) [33][34][35], quantile regressor as a probabilistic forecasting approach [36][37][38], and different related hybrid models [39][40][41][42][43][44][45][46]. ...
This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.
... His proposed stepwise and lasso regressors outperformed other benchmark models: ARIMA, ANN, exponential smoothing, and Nadaraya-Watson estimator [35][36][37]. For load forecasting, KNN and support vector machine (SVM) methods were implemented, whereas the feature selection was conducted by a DT regression and recursive feature elimination (RFE) [38]. Even though these above-mentioned ML standalone algorithms provide good results for energy forecasting, some algorithms often can not handle complex nonlinear relationships and have computational efficiency. ...
Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.
... ARX models are well-established in electricity price forecasting (EPF), as noted in [10,23]. The k-nearest neighbors algorithm has been found to be successful in the field of electricity market forecasts, mainly in forecasting electricity price and load [2,3,8,13,19,26] and renewable energy sources (RES) generation [25]. The proposed method is applied to the EPEX SPOT market in Germany. ...
The ongoing reshape of electricity markets has significantly stimulated electricity trading. Limitations in storing electricity as well as on-the-fly changes in demand and supply dynamics, have led price forecasts to be a fundamental aspect of traders’ economic stability and growth. In this perspective, there is a broad literature that focuses on developing methods and techniques to forecast electricity prices. In this paper, we develop a new hybrid method, called ARHNN, for electricity price forecasting (EPF) in day-ahead markets. A well performing autoregressive model, with exogenous variables, is the main forecasting instrument in our method. Contrarily to the traditional statistical approaches, in which the calibration sample consists of the most recent and successive observations, we employ the k-nearest neighbors (k-NN) instance-based learning algorithm and we select the calibration sample based on a similarity (distance) measure over a subset of the autoregressive model’s variables. The optimal levels of the k-NN parameter are identified during the validation period in a way that the forecasting error is minimized. We apply our method in the EPEX SPOT market in Germany. The results reveal a significant improvement in accuracy compared to commonly used approaches.
... Traditional forecasting has been thoroughly investigated and widely applied because of its high computational speed, robustness, and ease of implementation [10]. Machine learning methods and their expansions have been the subject of research interest for several decades; they include linear regression [11,12], multiple linear regression [13,14], and KNN [15]. For applications based on linear forecasting, these models are an excellent choice as they reflect the relationships among the features of output load and relevant factors. ...
With the wide use of the Internet of Things and artificial intelligence, energy management systems play an increasingly important role in the management and control of energy consumption in modern buildings. Load forecasting for building energy management systems is one of the most challenging forecasting tasks as it requires high accuracy and stable operating conditions. In this study, we propose a novel multi-behavior with bottleneck features long short-term memory (LSTM) model that combines the predictive behavior of long-term, short-term, and weekly feature models by using the bottleneck feature technique for building energy management systems. The proposed model, along with the unique scheme, provides predictions with the accuracy of long-term memory, adapts to unexpected and unpatternizable intrinsic temporal factors through the short-term memory, and remains stable because of the weekly features of input data. To verify the accuracy and stability of the proposed model, we present and analyze several learning models and metrics for evaluation. Corresponding experiments are conducted and detailed information on data preparation and model training are provided. Relative to single-model LSTM, the proposed model achieves improved performance and displays an excellent capability to respond to unexpected situations in building energy management systems.
This paper presents a novel deep neural network architecture, combining stacked LSTM and Time2Vec layers, to predict electricity prices up to eight hours ahead-an essential input for future decision-making tools. We assess this model using hourly wholesale electricity price data from Colombia, comparing it to state-of-the-art time series and machine learning forecasting methods, including SARIMA, Holt-Winters, XGBoost, and MLP. The results demonstrate that our model excels in accurately modeling non-linearity and explicitly characterizing data behavior, yielding more precise price predictions. Particularly, the Time2Vec layer significantly aids in capturing temporal relationships between input and output data. This framework shows promise in enhancing the precision of electric price forecasts, offering valuable insights for the energy sector’s decision-making.
Rapidly varying load demand is one of the greatest problems that distribution system operators are now experiencing. Many researchers have been implemented the load demand requirement using traditional methods and machine learning methods. Both the methods have their own pros and cons according to dataset available for a particular site. This paper aims to predict load demand using different stochastic and deterministic approaches for short term load forecasting (STLF). Utilizing a variety of dependent characteristics, historical load data for two years is collected from IEEE dataport. The methods used in this study are exponential smoothing of traditional method and machine learning methods such as support vector machine (SVM), ensemble, artificial neural network (ANN), convolution neural network (CNN), long-short term memory (LSTM) and CNN-LSTM hybrid Model. All of these methods are compared and the best option is chosen of coefficient of correlation (R). The best method is found to be CNN-LSTM hybrid with R of 95.05%. The algorithm is implemented in PYTHON platform which is more compatible for machine learning applications.
One of the biggest challenge that distribution system operators are facing today is unpredictable load demand. Various research methodologies including deterministic as well as stochastic techniques are used to determine the load. Although deterministic methods are effective but in certain new cases they fail to predict load accurately. This paper aims at determining load using various stochastic approaches for Short Term Load Forecasting (STLF). A historical data of load for 5 years is taken using various dependent parameters. This data is trained using various classifiers techniques. These classifier techniques include K Nearest Neighbours (KNN), Naive Bayes, Decision tree, Support Vector machines(SVM) and Ensembles. The best classifier technique is fed to various regression learners and the most effective regression learner is determined. The algorithms are implemented in PYTHON and MATLAB Platforms. The best method for both classifier and Regression is obtained as Ensemble with 95.1% accuracy and root mean square error of 0.02.
In this thesis, we proposed three system models to resolve the different problems related to energy optimization and anomaly detection. The exponential increase of EVs in smart cities has led to some complex trading problems. Therefore, in proposed system model, we deal with some major energy trading issues related to the charging of Electric Vehicles (EVs) in a Vehicular Energy Network (VEN). The enormous increase in the development of EVs and Intelligent Vehicles (IVs) has led to a complex network. When the number of vehicles increases, the number of links is also increased and generates intensive data. This complexity leads to insecure communication, road congestion, security and privacy issues in vehicular networks. Moreover, the detection of malicious IVs, data integration and data validation are also major concerns in VENs that hinder the network performance. To deal with these problems, we propose a blockchain based model for secure communication and detection of malicious IVs. In general, there are two major issues related to EVs charging. First, it is difficult to �find the nearest charging station with less energy consumption. Second, it is difficult to calculate the exact amount of energy needed to reach the nearest charging station from the current location of an EV. In traditional energy trading systems, centralized grids are being used where energy trading between EVs and charging stations is not secured. To deal with these issues, we proposed a consortium blockchain-based secure energy trading system with moderate charging cost. In the proposed work, the distance
of nearest charging station is calculated using k-Nearest Neighbor (KNN) technique. Furthermore, EVs also face various energy challenges such as imbalance load supply,
fluctuations in voltage level and energy scarcity from charging station. Therefore, a Demand Response (DR) strategy is used, which enables the EV users to flatten out the load curves and adjust the usage of electricity e�ciently.
Secondly, we addressed the problem of insecure communication in Vehicular Network (VN). The integration of blockchain with vehicular network makes the network secure and trustful. For the authentication of IVs, Certi�ficate Authority (CA) is used, and InterPlanetary File System (IPFS) is integrated with CA to resolve the issue of storage. A reputation mechanism is introduced to detect the malicious IVs in the network based on their ratings. A branching concept is involved where the validated transactional data and the malicious data of IVs is stored in two separate chains: Integrity Chain (I-Chain) and Fraud Chain (F-Chain). In third contribution of our thesis, we deal with some major problems related to the �financial sector such as fraud and anomalies. These are common problems in E-banking and online transactions. Anomaly detection is a well known method to �find frauds and misbehavior in the �financial sector. However, with the advancement of �financial sectors, the methods of fraud and anomalies are also getting advanced. Furthermore, blockchain technology is introduced as the most secure technology and integrated with �finance. However, besides these advanced
technologies, there still exist many fraudulent cases every year. Therefore, we proposed a fraud detection prediction model based on machine learning and blockchain. Machine learning techniques train the dataset according to the fraudulent and integrated transaction patterns and predict the new incoming transactions. Blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in �financial sectors. There are two machine learning algorithms: XGboost and Random Forest (RF) are used for the classi�fication of transactions. These models provide 99% accurate results to �find the frauds and anomalies in the given dataset. The proposed models are also able to predict the transaction patterns. We also calculate the precision and AUC of models to measure the accuracy. Simulation results show that our proposed integrated models outperforms the traditional techniques in terms of accuracy and security. A security analysis of proposed smart contracts is also shown in simulation section. The security analysis is used to identify the vulnerabilities of smart contracts. Furthermore, four attacker models are introduced to resolve the issue of sel�fish mining attack, double spending attack, replay attack, and Sybil attack.
Smart Grid (SG) is a modernized grid that provides efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world and almost everything relies on it. As smart devices are increasing dramatically with the rapid increase in population,
there is a need for an efficient energy distribution mechanism. Furthermore, the forecasting
of electricity consumption is supposed to be a major constituent to enhance the performance
of the SG. Various learning algorithms have been proposed in the literature for efficient load
and price forecasting. However, there exist some issues in the proposed work like increased
computational complexity. The sole purpose of the work done in this thesis is to efficiently
predict electricity load and price using different techniques with minimum computational
complexity. Chapter 1 provides an introduction of various concepts present in the power
grids. Afterwards, the unified system model, different sub-problems and the contributions
made in the thesis are also presented. Chapter 2 discusses the existing work done by different
researchers for performing electricity load and price forecasting. In Chapter 3, Enhanced
Logistic Regression (ELR) and Enhanced Recurrent Extreme Learning Machine (ERELM)
are proposed for performing short-term load and price forecasting. The former is an enhanced
form of Logistic Regression (LR); whereas, the weights and biases of the latter are optimized
using Grey Wolf Optimizer (GWO). Classification And Regression Tree (CART), Relief-F
and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the
basis of selected features, classification is performed using ELR. Moreover, cross validation
is done using Monte Carlo and K-Fold methods. In order to ensure optimal and secure
functionality of Micro Grid (MG), Chapter 4 focuses on coordinated energy management of
traditional and Renewable Energy Sources (RES). Users and MG with storage capacity are
taken into account to perform efficient energy management. A two stage Stackelberg game is formulated. Every player in the game tries to increase its payoff, and ensure user comfort and system reliability. Furthermore, two forecasting techniques are proposed in order to forecast Photo-Voltaic Cell (PVC) generation for announcing optimal prices. Both the existence and uniqueness of Nash Equilibrium (NE) for the energy management algorithm are also
considered. In Chapter 5, a novel forecasting model, termed as ELS-net, is proposed. It is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model
Ensemble Bi Long Short Term Memory (EBiLSTM) forecasting technique and Support
Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear
and non-linear Intrinsic Mode Functions (IMFs). EBiLSTM is used to forecast the non-linear
IMFs and SVM is employed to forecast the linear IMFs. The usage of separate forecasting
techniques for linear and non-linear IMFs decreases the computational complexity of the
model. In Chapter 6, a novel deep learning model, termed as Gated-FCN, is introduced for
short-term load forecasting. The key idea is to introduce automated feature selection and
a deep learning model for forecasting, which includes an eight layered FCN (FCN-8). It
ensures that hand crafted feature selection is avoided as it requires expert domain knowledge.
Furthermore, Gated-FCN also helps in reducing noise as it learns internal dependencies
as well as the correlation of the time-series. Enhanced Bidirectional Gated Recurrent Unit
(EBiGRU) model is dovetailed with FCN-8 in order to learn temporal long-term dependencies
of the time-series. Furthermore, weight averaging mechanism of multiple snapshot models is
adapted in order to take optimized weights of BiGRU. At the end of FCN-8 and BiGRU, a
fully connected dense layer is used that gives final prediction results. The simulations are
performed and the results are provided at the end of each chapter. In Chapter 3, the simulations
are performed using UMass electric and UCI datasets. ELR shows better performance with
the former dataset; whereas, ERELM has better accuracy with the latter. The proposed
techniques are then compared with different benchmark schemes. The comparison is done
to verify the adaptivity of the proposed techniques. The simulation results show that the
proposed techniques outperform the benchmark schemes and increase the prediction accuracy
of electricity load and price. Similarly, in Chapter 4, simulations are performed using Elia,
Belgium dataset. The results clearly show that the proposed game theoretic approach along
with storage capacity optimization and forecasting techniques give benefits to both users and
MG. In Chapter 5, simulations are performed to examine the effectiveness of the proposed
model using two different datasets: New South Wales (NSW) and Victoria (VIC). From the
simulation results, it is obvious that the proposed ELS-net model outperforms the benchmark
techniques: EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms
of forecasting accuracy and minimum execution time. Similarly, the simulation results
of Chapter 6 depict that Gated-FCN gives maximum forecasting accuracy as compared to
the benchmark techniques. For performance evaluation of the proposed work, different
performance metrics are used: Mean Absolute Percentage Error (MAPE), Mean Absolute
Error (MAE), Mean Squared Error (MSE) and Root Mean Square Error (RMSE). The overall
results prove that the work done in this thesis outperforms the existing work in terms of
electricity load and price forecasting, and computational complexity.
We present in this paper a model for forecasting short-term electric load based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated to improve the forecast results. Further, a two-stage ensemble strategy is used to enhance the generalization capability of the proposed model. We also apply the proposed model to probabilistic load forecasting using Monte Carlo dropout. Three public datasets are used to prove the effectiveness of the proposed model. Multiple test cases and comparison with existing models show that the proposed model is able to provide accurate load forecasting results and has high generalization capability.
Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.
Day-ahead electricity price forecasting (DAEPF) plays a very important role in the decision-making optimization of electricity market participants, the dispatch control of independent system operators (ISOs) and the strategy formulation of energy trading. Unified modeling that only fits a single mapping relation between the historical data and future data usually produces larger errors because the different fluctuation patterns in electricity price data show different mapping relations. A daily pattern prediction (DPP) based classification modeling approach for DAEPF is proposed to solve this problem. The basic idea is that first recognize the price pattern of the next day from the “rough” day-ahead forecasting results provided by conventional forecasting methods and then perform classification modeling to further improve the forecasting accuracy through building a specific forecasting model for each pattern. The proposed approach consists of four steps. First, K-means is utilized to group all the historical daily electricity price curves into several clusters in order to assign each daily curve a pattern label for the training of the following daily pattern recognition (DPR) model and classification modeling. Second, a DPP model is proposed to recognize the price pattern of the next day from the forecasting results provided by multiple conventional forecasting methods. A weighted voting mechanism (WVM) method is proposed in this step to combine multiple day-ahead pattern predictions to obtain a more accurate DPP result. Third, the classification forecasting model of each different daily pattern can be established according to the clustering results in step 1. Fourth, the credibility of DPP result is checked to eventually determine whether the proposed classification DAEPF modeling approach can be adopted or not. A case study using the real electricity price data from the PJM market indicates that the proposed approach presents a better performance than unified modeling for a certain daily pattern whose DPP results show high reliability and accuracy.
Short-term load and price forecasting is an important issue in the optimal operation of restructured electric utilities. This paper presents a new intelligent hybrid three-stage model for simultaneous load and price forecasting. The proposed algorithm uses wavelet and Kalman machines for the first stage load and price forecasting. Each of the load and price data is decomposed into different frequency components, and Kalman machine is used to forecast each frequency components of load and price data. Then a Kohonen Self Organizing Map (SOM) finds similar days of load frequency components and feeds them into the second stage forecasting machine. In addition, mutual information based feature selection is used to find the relevant price data and rank them based on their relevance. The second stage uses Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting of load and price frequency components, respectively. The third stage machine uses the second stage outputs and feeds them into its MLP-ANN and ANFIS machines to improve the load and price forecasting accuracy. The proposed three-stage algorithm is applied to Nordpool and mainland Spain power markets. The obtained results are compared with the recent load and price forecast algorithms, and showed that the three-stage algorithm presents a better performance for day-ahead electricity market load and price forecasting.
Electricity price forecasting proves useful for power producers and consumers to make proper decisions in a market-oriented environment. However, due to the complex drivers and sharp fluctuation of electricity prices, accurate electricity price forecasting turns to be very difficult. To better capture the characteristics of day-ahead electricity prices, a new integrated model based on the improved empirical mode decomposition (IEMD), autoregressive moving average with exogenous terms (ARMAX), exponential generalized autoregressive conditional heteroscedasticity (EGARCH) and adaptive network-based fuzzy inference system (ANFIS) is proposed in this paper. Then it is validated by using the data from Spanish and Australian electricity markets. The results indicate that the forecasting accuracy of the new integrated model proves higher than that of some well-recognized models in the literature.
This paper depicted the novel data mining based methods that consist of six models for predicting accurate future heating and cooling load demand of water source heat pump, with the objective of enhancing the prediction accuracy and the management of future load. The proposed model was developed to ease generalization to other buildings, by making use of readily available measurements of a comparatively small number of variables related to water source heat pump operation in the building environment. The six models are - Tree Bagger, Gaussian process regression, multiple linear regression, Bagged Tree, Boosted Tree and neural network. The input parameter comprised the prescribed period, external climate data and the diverse load conditions of water source heat pump. The output was electrical power consumption of water source heat pump. In this study, simulations were conducted in three sessions - 7-day, 14-day and 1-month from 8th July to 7th August 2016. The forecast precisions of data mining models were measured by diverse indices. The performance indices which were used in assessing the prediction performance were - mean absolute error, coefficient of correlation, coefficient of variation, root mean square error, mean square error and mean absolute percentage error. The Mean absolute percentage error results for 7-day future energy demand forecasting from Tree Bagger, Gaussian process regression, Bagged Tree, Boosted Tree, neural network and multiple linear regression were 3.544%, 0.405%, 1.703%, 1.928%, 2.592% and 13.053%, respectively. Moreover, when the proposed data mining model performance was compared with the existing studies, the mean absolute percentage error of 2.515% was found out for the first session, 7-day. The results also showed that the six models were efficient in foreseeing the abnormal behavior and future cooling and heating load demand in the building environment.
Energy usage and demand forecasting is an essential and complex task in real time implementation. Proper coordination is required between the consumer and power companies for monitoring, scheduling and operating the electrical devices without any damages. In this paper, we propose a novel neural network based optimization approach for energy demand prediction. Initially, the Conventional Neural Network (CNN) approach is employed to find the required energy demand prediction at the consumer end. Secondly, Neural Network based Genetic Algorithm (NNGA) and Neural Network based Particle Swarm Optimization (NNPSO) approaches are implemented where the weights of the neural network are automatically adjusted. Closer observation from the result reveals that the proposed NNGA approach performs better for short term load forecasting and proposed NNPSO is more suitable for the long term energy prediction. For the experimental results, real time data are taken from pecan street (Pecan Street Inc.). From the simulations, it can be concluded that the proposed optimization approach algorithm yield better results than the CNN approach in predicting the future energy demand. Further, the result reveals that it is possible to manage the demand and supply, planning of power grid and prediction of future energy requirement in the smart grid.