Load Forecasting In Mageta Island Microgrid System, Kenya, with a 5 kw Peak Load using Artificial Neural Network

Transcription

1 Load Forecasting In Mageta Island Microgrid System, Kenya, with a 5 kw Peak Load using Artificial Neural Network J. M. Mbuthia*, C.M. Kiruki* *Department of Electrical and Information Engineering University of Nairobi, Kenya Abstract This paper presents an artificial neural network (ANN) algorithm for load forecasting in a small island community microgrid, in Lake Victoria in Kenya, with a peak load of less than 5kW. The aim is to investigate the suitability of neural networks for forecasting in small power installations. The paper aims to strip down the amount and variety of data used for forecasting. Only historical load data is used with the days divided into weekdays and weekends. Feed-forward ANN and the Levenberg-Marquardt algorithm is used. The results suggest that this historical data is sufficient to provide an acceptable 24-hr ahead forecast. This approach is significant in that various weather forecasts such as temperature and humidity may not be readily available for particular sites as they cover very small geographical areas that may possess variations of the regional and national weather forecasts. More so, this particular microgrid has a load composition that is highly independent of weather conditions. Air conditioners and heaters are non-typical loads in this system. A Mean Absolute Percent Error of less than 20% is achieved using historical data only. Keywords: Artificial Neural Network (ANN), Levenberg- Marquardt, low load forecasting, Microgrid I. INTRODUCTION According to World Energy Outlook 2014 report by International Energy Agency, an estimated 620 million people in sub-saharan Africa have no access to electricity. As a result, microgrids and minigrids have gained momentum due to these low levels of grid connectivity. The major sources of energy in such installations are solar and wind power. The intermittency of such major sources of energy necessitates proper management of the system. Of critical importance is the ability to forecast both the energy source and load demand in such a system. This will be essential in that it forms an integral part of the energy management system. The microgrid used in this study is located in Mageta Island in Kisumu, Kenya. It is operated by SteamCo Company and has a peak load of less than 5 kw. This paper investigates the application of an Artificial Neural Network (ANN) to such small microgrids. Load demand forecasting using ANN has been widely carried out for large regional and national power grid networks. Due to the aggregation of many loads in the order of MW spanning wide geographical areas, the demand curve tends to vary smoothly and follows a quick to learn and predict pattern. Consequently, load forecasting in such systems is highly accurate with a Mean Absolute Performance Error of less than 3% being reported. Al-Shareef et al in [1] used ANN for a 24-hr load forecast for the western area of Saudi Arabia with an average load of over 8000MW. In addition to historical data, weather elements (forecasted temperature and humidity) and day type were also used as inputs to the network. A MAPE of 1.82% was achieved. Rajasekaran and Subbaraj in [2] performed ANN based hourly load forecast for the power system of Chennai city (Tamilnadu state India) with an average load of 5000 MW. Historical data and weather information was used as the inputs to the network. A MAPE of 2.95% was achieved. Tomonobu et al in [3] used ANN to perform a 1-hr ahead load forecasting using load power data of Okinawa Electric Power Company with an average of 800 MW. Historical load data and temperature data were used as inputs. The neural network was trained by using the data of past 30 days from the day before forecast day and past 60 days before and after forecast day in previous year. A MAPE of 1.63 % was achieved. In contrast to the large grid networks presented above, the load in microgrids is small and consists of a small number of aggregated loads. Thus a variation in one load has a significant effect on the aggregated load than it would have on a larger national grid network. Load forecasting has been carried out for varying sizes of microgrids and the results suggest that as the load size decreases, the MAPE (forecasting error) increases. Two of these studies are briefly presented. Andrei et al in [4] applied a NN for forecasting for 230 houses with a peak load of 340 kw and 90 houses with a peak of 140 kw. The average NRMSE for the former was found to be 3.05% and for the latter, 3.82%. Llanos et al in [5] used a NN for load forecasting in an isolated village with a load peak of 15 kw and obtained a 13.8% MAPE. Thus it s clear that as the load/demand to be forecasted reduces and becomes noisier, the less accurate the forecasting becomes. For a microgrid of less than 5 kw peak load, the forecasting error is expected to be bigger than that in the afore-mentioned studies. However, with the right data inputs for training and data pre-processing, an ANN can be successfully applied in such small installations as will be presented in this study

2 II. NEURAL NETWORK ARCHITECTURE Neural network is a machine learning algorithm that is suitable for complex non-linear problems. Given a set of inputs, the network can predict the output whose accuracy is determined by the network parameters. In supervised learning, the net work is first trained. This implies there is a set of data with inputs and corresponding outputs known apriori. The inputs of this set of data are fed into the network and its parameters are adjusted till the predicted outputs closely match the real outputs. The parameters here include the number of inputs, hidden layers, nodes (neurons) in each layer and the weights mapping the transition from one layer to the next. The architecture of a network refers to the number of neurons, their arrangement and connectivity. Each neuron receives input from the neighbouring neurons connected to it, processes the information and produces an output. The feedforward and back propagation neural network architecture in Fig.1 was used in this study. The neural network has 3 layers with 15 neurons in the hidden layer. This allows the possibility of implementing the solution on an embedded platform. The Mageta Island is situated along the equator and thus temperature variations are moderate throughout the year. More so, the system serves a few households and kiosks and thus barely has loads that depend on weather conditions such as AC systems. Consequently, no weather conditions were taken into account in the forecasting. This eliminates need for additional sensors in the system and knowledge of future weather conditions, which may not be readily available in some places. points in the system. This allows remote monitoring and real time data logging. Specifically, the meters are used to measure energy from the solar panels and wind turbine, energy in and out of the batteries and energy into the inverter (load). This data is relayed through the cloud to a dashboard where further data processing and analysis can be carried out. Fig.2 and Fig.3 depict the custom meters and the data visualization in the dashboard respectively. The microgrid is new and has been in operation for barely a year. It is a 24V, approximately 5 KW system with the following components: Solar: 2 arrays, each of 12 x 235W 24V panels Charge Controller: 2 x 60A MPPT controllers Batteries: 8 x 200AH 6V lead acid flooded in two strings. Inverter: 24V 4100VA continuous output Wind turbine: 1kW rectified to DC in the cabin and controlled by a dump load. Standby diesel generator Input Layer Hidden Layer Output Layer Figure 2: Custom Energy Meters x 1 a 1 (2) Different inputs for Models 1-5 x 2 x m a 2 (2) a n (2) a 1 (3) Forecast Figure 1: Neural Network Architecture MATLAB Neural Network Toolkit has been used for the training of the network. This kit uses the Levenberg- Marquardt (LM) algorithm for the back propagation training. It provides a numerical solution to the problem of minimizing a nonlinear function. LM blends the stability of the steepest descent method and the speed of the Gauss- Newton algorithm. Martin and Menhaj in [6] observed that the Marquardt algorithm was very efficient when training networks that have up to a few hundred weights. III. DATA LOGGING Custom DC meters with remote communication capabilities have been built and installed at various energy Figure 3: Dashboard Visualization 477

3 The custom remote monitoring system has been installed at Mageta Island microgrid. Data considered for load forecasting spanned five months commencing February 2016 to June Weekends and holidays have not been factored in the network training. Due to the location of the microgrid system, any holiday load would closely follow the weekend load profile. The number of weekends and holidays available in 5 months is very small to provide adequate training and testing of a neural network. Therefore, only the weekdays data has been used extensively for the forecasting phase of this study. Data pre-processing The data needs some pre-processing before being used in the ANN. The data is updated every 10 minutes and thus for every hour there are 6 data points. This translates to 144 data points for a full day. While this gives a more accurate picture of the real time operation of the site, it would be computationally expensive to train the network with such a huge number of inputs. Therefore, hourly averages are calculated resulting in 24 data points for a full day. The result of such averaging is presented in Fig. 4. Missing data is filled through extrapolation. Figure 4: Hourly Data Averaging IV. NETWORK TRAINING AND TESTING The network training and testing is done under two tests. In Test One, the goal is to determine the nature of the historical data that gives the best forecasting capability. That is, for example, whether considering the previous 5 days load data gives a better prediction for today s load or is yesterday s load enough to predict today s load. In Test Two, the goal is to determine the amount of historical data adequate to train the network. That is, for example, whether training the network with 3 months data is better than training it with a single month s data. A. Test One A month s historical data is used to train the network. 28 weekdays load data commencing 29 th Feb to 6 th April was used. As earlier noted, the weekends have been omitted in this training data. The weekday network model was tested for five different training models: Model 1: The data for the previous four consecutive weekdays and the same day one week before was used for training the network. To forecast data for a day i.e. 24 hours ahead load, the previous 5 weekdays load was used as input. Thus there are 5 neurons in the input layer. Model 2: Data for the previous five consecutive days and 1hr before, 2hrs before, 3hrs before the hour of interest for each of the five days was used. Thus there are 20 neurons in the input layer. Model 3: Data for the previous three consecutive weekdays is used. This results in 3 neurons at the input layer. Model 4: Data for the previous two consecutive weekdays is used. This results in 2 neurons at the input layer. Model 5: Data for the previous two consecutive weekdays and 1hr before, 2hrs before, 3hrs before the hour of interest for each of the two days was used. Thus there are 8 neurons in the input layer. For testing the network, data that was not used in the training was considered. E.g. to forecast the data for 14 th March, the neural network would require data for the previous 5 weekdays (Model 1 and 2) dating back to 9 th March. Since the training phase used data for 28 weekdays starting 29 th Feb to 6 th April, the model was tested for dates starting 14 th April. Thus to forecast the load for 14 th April (Thursday) using model 1, the load for the previous 5 weekdays was required as the input to the neural network. These weekdays are: 13 th, 12 th, 11 th, 8 th, 7th; with 9 th and 10 th omitted since they fall on a weekend. This ensured that no data used in the training process was used for the testing phase. B. Test Two Test One is only concerned with how the various input models affect the accuracy of the neural network. The network was trained with a constant number of historical load data, that is, 28 weekdays. In this test, the additional effect of the number of days used to train the network is considered. In addition to evaluating the various models, the network will be trained using 30, 40 and 60 days. The network is tested by forecasting the load for 13 th, 14 th and 15 th June V. RESULTS AND DISCUSSION The forecasting results are evaluated using the Mean Absolute Percent Error of the formula: MAPE = ( 1 N Actual i Forecast i N i=1 ) 100 (1) Actual i where N is the number of forecasting points. The table below shows the results obtained for test one: 478

4 Table 1: Test One Results Date Model 1 Model 2 Model 3 Model 4 Model 5 14/4/ /4/ /4/ /4/ /4/ Weekdays Average Weekend Average The data highlighted in purple indicates the weekend data and as expected, the forecasting error as calculated using equation (1) is higher as compared to that of the weekdays. This is because the neural network has been trained using weekdays data only and thus forecasting a weekend load using this network would result in higher errors. This shows that it s better to use two separate networks for weekdays and weekends load forecasting. As earlier noted, there weren t enough weekend data to sufficiently train the network. It s evident that Model 2 which considers the previous 5 days and 1hr before, 2hrs before, 3hrs before the hour of interest performed relatively poorly. Model 3 which considers data for only the previous 3 days had the best performance. The graphs below show the results for 24 hr ahead load forecasting for 14 th, 17 th and 18 th March 2016 using model 1. Figure 6: Actual and Forecast Load for 15 April, 2016 In Test Two, the following tables show the results for every model. Table 2 : Model 1 No. of Training 30 Days 40 Days 60 Days Days 13/06/ /06/ /06/ AVERAGE Table 3: Model 2 13/06/ /06/ /06/ AVERAGE Table 4: Model 3 13/06/ /06/ /06/ AVERAGE Table 5: Model 4 13/06/ /06/ /06/ AVERAGE Figure 5: Actual and Forecast Load for Apr 14, 2016 Table 6 : Model 5 13/06/ /06/ /06/ AVERAGE

5 The following graphs show the results for 15 th June using Model 5 with various training days: network is accumulated and thus the more accurate the forecasting gets. Figure 7: Actual and Forecast Load with 30 Days Training VI. CONCLUSION A MAPE performance of less than 20 % has been achieved in the load forecasting. Given that the peak load recorded in the period of observation is less than 4 kw, this is an acceptable result. From the literature review, it is noted that the accuracy of forecasting decreases as the load size decreases. Most forecasting has been carried out for large systems in the range of tens of MW with a MAPE of less than 3%. This shows that the forecasting approach taken in this study will give even better results if undertaken in a larger microgrid system. From this study, it s evident that models 3, 4 and 5 are generally more suited in making 24 hr ahead load forecast. Therefore, knowledge of the previous two or three days load is sufficient to make adequately accurate forecast of the present day load profile. In addition, the study gives an insight that the more historical data used in training the network, the better the accuracy in forecasting. This is evidenced by the relatively better accuracy reported when using 60 training days in all the five models. Consequently, the older the microgrid monitoring system gets, the more the historical data for training the network is accumulated and thus the more accurate the forecasting gets. Figure 8: Actual and Forecast Load with 40 Days Training Figure 9: Actual and Forecast Load with 60 Days Training From this Test 2, it s evident that models 3, 4 and 5 are generally more suited in making 24 hr ahead load forecast. This is supported by the results obtained in Test 1. Therefore, knowledge of the previous two or three days load is sufficient to make adequately accurate forecast of the present day load profile. In addition, Test 2 reveals that the more historical data used in training the network, the better the accuracy in forecasting. This is evidenced by the relatively better accuracy reported when using 60 training days in all the five models. Consequently, the older the microgrid system gets, the more the historical data for training the REFERENCES [1] A.J. Al-Shareef, E.A. Mohamed, and E. Al-Judaibi, "Next 24-Hours Load Forecasting Using Artificial Neural Network (ANN) for the Western Area of Saudi Arabia," JKAU: Engineering Sciences, pp , [2] Dr. P. Subbaraj and V. Rajasekaran, "Short Term Hourly Load Forecasting Using Combined Artificial Neural Networks," in IEEE International Conference on Computational Intelligence and Multimedia Applications, [3] Tomonobu Senjyu, Hitoshi Takara, Katsumi Uezato, and Toshihisa Funabashi, "One-Hour-Ahead Load Forecasting Using Neural Network," IEEE Transactions On Power Systems, vol. 17, no. 1, pp , February [4] Andrei Marinescu, Colin Harris, Ivana Dusparic, Siobhan Clarke, and Vinny Cahill, "Residential Electrical Demand Forecasting in Very Small Scale: An Evaluation of Forecasting Methods," in Proceeds of IEEE 2nd International Workshop on Software Engineering Challenges for the Smart Grid (SE4SG), San Francisco, CA, 2013, pp [5] J. Llanos, D. Sáez, R. Palma-Behnke, A. Núñez, and G. Jiménez-Estévez, "Load profile generator and load forecasting for a renewable based microgrid using Self Organizing Maps and neural networks," in Proceeds of IEEE 2012 International Joint Conference onneural Networks (IJCNN), Brisbane, QLD, 2012, pp [6] Martin T. Hagan and Mohammad B. Menhaj, "Training Feedforward Networks with the Marquardt Algorithm," IEEE Transactions On Neural Networks, vol. 5, no. 6, pp , November [7] Maryam Ramezani, Hamid Falaghi, and Mahmood-Reza Haghifam, "Short-Term Electric Load Forecasting Using Neural Networks," in Proceeds of EUROCON 2005.The International Conference on Computer as a Tool, Belgrade, Serbia, Novemeber,2005, pp

6 [8] Albert Molderink, Vincent Bakker, Maurice G.C. Bosman, Johann L. Hurink, and Gerard J.M. Smit, "Management and control of domestic smart grid technology," IEEE Transactions on Smart Grid, vol. 1, no. 2, pp , September [9] Mara Lficia M. Lopes, Carlos R. Minussi, and Anna Diva P. htufo, "A Fast Electric Load Forecasting Using Neural Networks," in Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems, Lansing MI, 2000, pp Mwangi Mbuthia is an Associate Professor and currently the Dean, School of Engineering at the University of Nairobi. He received a B.Sc. (First Class Honors) in Electrical and Electronic Engineering in 1976 from the University Nairobi, An M.Sc. and DIC in 1978 from Imperial College of Science Technology and Medicine in London, and a Ph.D in 1985 from the University of Manchester in England. His research interests include broadband last mile connectivity devices, special materials and devices for solar power applications, IoT devices, smart sensor and artificial intelligence applications in energy delivery automation and smart grids. He has developed many products and systems and published widely. Kiruki Cosmas Raymond received a B.Sc. (First Class Honors) in Electrical and Electronic Engineering in 2014 from the University of Nairobi, Kenya. Currently, he is a Research Assistant at the Electrical and Information Engineering department at the University of Nairobi, where he is pursuing an M.sc. in Eletrical and Electronic Engineering. His research interests include IoT and embedded systems design, artificial intelligence and machine learning and their applications to renewable energy technologies and smart grids